Health systems are not stepping into artificial intelligence from a place of calm or control. In the United States, spending now exceeds $4.5 trillion, with a significant share tied up in administrative work that adds complexity more than clarity. Clinicians are caring for more patients, navigating more data and making more decisions under pressure than ever before. The system is stretched. Artificial intelligence is entering at a moment when change is no longer a choice.

The discussion drew on the experience of three leaders who are not observing this shift. They are guiding it. Hal Wolf leads HIMSS, influencing digital health policy and implementation across more than 100 countries. Isaac Kohane, MD, PhD, Chair of Biomedical Informatics at Harvard Medical School, has spent four decades defining how data informs clinical care. Ran Balicer, MD, Chief Innovation Officer at Clalit Health Services, operates within one of the world’s most integrated health systems, where data and care are aligned across generations.

These are not just star panelists. They are system-wide architects.  What emerged from the hour-long conversation was not what artificial intelligence can do. It was a recognition that it is already doing more than most systems are prepared to guide and govern.

Dr. Kohane captured the tension immediately. “I think that we have to worry about the fact that we’re going both too slow and too fast.”

That statement reflects a reality many leaders feel but rarely express. Governance takes time because it must. Patient safety, validation and accountability require structure. Practice moves in real time. Clinicians do not have the luxury of waiting for perfect systems.

“They’re so desperate to do right by their patients to use other resources,” Dr. Kohane adds.

That instinct is not a weakness. It reflects a commitment to doing what is right for the patient. When clinicians turn to external AI tools, they are seeking clarity, speed, and confidence in their decisions. Artificial intelligence is already present at the point of care, shaping how physicians assess information, validate thinking, and move forward. The system is not adopting AI. The system is catching up.

This creates a condition that is difficult to measure and even harder to manage. Different clinicians use different ChatGPT platforms. Those tools produce different answers. Different assumptions shape those answers. Over time, consistency erodes. The system begins to operate with multiple definitions of truth (and the risk of varied outcomes).

Dr. Kohane’s warning is not about misuse. It is about misguided permanence. “The worst outcome will be if the worst parts of medicine get concrete poured over it, by AI.”

Artificial intelligence does not fix a system; without leadership, it accelerates the integration of incorrect assumptions. If workflows are inefficient, they become more efficiently inefficient. If bias exists in data, it becomes more precise. If fragmentation defines care, it scales.

This is not a failure of technology. It is a mirror held up to system-wide leadership.

Hal Wolf, among the health sector’s leading policy and operational voices, grounded this moment in proven experience. Health care has seen this pattern before. When internet connectivity entered hospitals, clinicians moved faster than governance. They created access where it was needed. Systems responded later. Risks were discovered after adoption.

Artificial intelligence now follows that same trajectory, though at far greater speed and with far greater consequences. Web connectivity gave quick access to information. Artificial intelligence influences how that information is interpreted and acted upon.

“We have to go faster,” Mr. Wolf said. “But there needs to be structure around it.”

That is the leadership challenge of this moment. Speed without structure creates exposure. Structure without speed creates irrelevance. The tension between the two is not something to resolve. It is something to manage continuously.

The industry has predictably responded to artificial intelligence. It has started where risk is lowest and return is clearest. Documentation, scheduling and revenue cycle optimization have become the entry points. These applications reduce burden and improve efficiency. They are necessary. However, they are not transformational.

The shift occurs when artificial intelligence moves into clinical decision-making. At that point, the question is no longer whether the system works. The question becomes whether it should be trusted.

Who owns a decision informed by an algorithm? How is accuracy validated? What happens when a clinician disagrees with a recommendation? These are not technical questions. They are questions of accountability. Artificial intelligence does not assume responsibility. It does not carry consequence. That remains with leadership.

Dr. Balicer reframed the conversation, shifting how the room thought about artificial intelligence. “There’s no such thing as AI neutrality. Algorithms are just opinions embedded in code.”

That insight is easy to acknowledge and difficult to operationalize. Every model reflects choices. What data is included? What outcomes are prioritized? What trade-offs are accepted? Those decisions are embedded in the system, shaping how it interprets information.

When a health system adopts an AI tool, it is not simply implementing technology. It is adopting a perspective.

At Clalit Health Services, alignment across payer and provider creates a system where priorities are consistent. Even there, external AI models introduce new assumptions. Those assumptions may not align with the system’s goals. If leadership does not define its own values, it inherits someone else’s.

This becomes real in proactive care. Artificial intelligence enables systems to identify patients at risk before they present. It allows for earlier intervention, often improving outcomes.

It also creates a new kind of pressure. “The toughest choice is what not to do,” Dr. Balicer said.

That statement deserves more attention than it receives. Health care has been built around responding to need. Artificial intelligence introduces the ability to anticipate it. When every patient can be flagged, every risk predicted and every intervention suggested, the system is no longer constrained by insight. It is constrained by capacity.

Artificial intelligence expands what can be done. It does not expand who can do it. Leadership becomes the act of choosing who does what based on validated data.

There is a moment that captures this shift. Imagine a primary care physician starting the day not with a schedule of patients who have called for appointments, but with a list generated by AI identifying individuals who are likely to experience clinical complications in the next six months. Some will develop chronic conditions. Some will require hospitalization. Some can be helped now – preventively.

The physician cannot see them all. Artificial intelligence expands what is possible. Leadership decides what is essential and permissible.

The industry often responds to complexity with activity. Organizations pilot, test and explore. They engage broadly without committing deeply. This creates motion. It rarely creates progress. Pilots are nothing more than experiments. At some point, leadership must decide what to scale, what to stop and what defines value.

Hal Wolf grounded the conversation in discipline. Without a defined, shared objective, effort becomes noise. Pilots create learning, though they often avoid decision-making. Leadership requires clarity. What problem are we solving? What outcome defines success? What are we willing to prioritize? Without those answers, artificial intelligence adds another layer of complexity to an already complex system.

Dr. Kohane brought the conversation back to the discipline of leadership. It cannot remain abstract. It must be informed by experience.

“Go and pay a few bucks and use three or four of the models… get a feel for what this does,” Dr. Kohane advised.

That is not a call for technical fluency. It is a call for leadership proximity. Leaders cannot guide what they do not understand. Artificial intelligence does not behave consistently across models. It produces different answers, shaped by different assumptions. Without direct engagement, those differences remain hidden, and leadership becomes removed from the very decisions it is responsible for guiding.

This is where many organizations hesitate. Artificial intelligence feels complex and complexity invites delegation. At this moment, delegation creates distance. Leadership is required to move closer, not further away.

Artificial intelligence is not reducing the role of leadership. It is redefining it.

This is not a gradual transition. It is already underway. Artificial intelligence is embedded in workflows, shaping decisions and influencing behavior in real time. The system is adapting whether leadership is ready or not.

The question is no longer whether artificial intelligence will shape the future of health. It will. The question is whether leadership will shape how it is applied.

Artificial intelligence will not fix health. It will scale whatever we allow it to touch. The question is whether it will scale what is best in health or what we have yet to fix.

The post AI Will Not Fix Health Care – Leadership Might appeared first on Medika Life.

CNS Summit: A Community, Not a Conference

The CNS Summit is less an event and more a movement, the roughly 900-person ecosystem of leaders, scientists, entrepreneurs, and communicators is bound by purpose. Through the years, it has become an “industry reunion” for those working at the intersection of science and humanity. Attendees don’t simply show up for “unscripted” presentations or networking; they come to commune, share ideas, listen and support each other’s journey.

Founded and carefully curated by Amir Kalali, MD, a former Quintiles executive, who focuses on the intersection of life science and technology, believes collaboration unleashes humanity’s greatest potential, Summit operates on the belief that innovation in clinical research and drug development depends on curiosity and connection.  Summit cultivates an atmosphere where hierarchy dissolves. CEOs engage with early-career professionals. Startups find champions among seasoned executives. Conversations flow freely, often long after formal sessions end. As the website declares, “The Summit brings together a curated group of top decision makers from pharma, biotech, CROs, investigator sites, patient advocacy groups, investors and other stakeholders.”

The site also notes a key differentiator for the 2025 gathering: “Networking tables throughout the day. A dedicated space for connection and conversation throughout Summit.” The Summit agenda reinforces that the gathering isn’t just about sessions; it’s about forging connections.

This is the kind of conference where attendees return year after year, sometimes at personal expense, because they recognize that the ROI is more than professional – it’s personal. You leave Boston with new insights, renewed energy and, often, lifelong friends.  What makes CNS Summit unique is that the “price of admission” includes year-long networking gatherings sponsored by Summit community leaders.

“When people introduce themselves at Summit, I want to hear about them — their backstory motivation first, and only then about the problem they are looking to solve,” shares long-time Summit attendee Craig Lipset, DTRA.org Co-Chair, and an advisor to global health innovation enterprises. “This is an event that cherishes long-term relationships between people, which is why this space has become so critical during such a volatile time in the industry,” he adds.

Communication Tip: At CNS Summit, authenticity amplifies influence. Don’t arrive with a set corporate pitch; come ready to share experiences and learn from others. This is a platform for vulnerability, curiosity and conversation, not self-promotion. In a community built on trust, the most powerful communication skill is listening.

The JP Morgan Healthcare Conference: The Ecosystem’s Main Stage

If the CNS Summit is a retreat for reflection, the JP Morgan Healthcare Conference is the watering hole of the life science world, crowded, noisy and absolutely vital. JP Morgan frames it as the “largest and most informative healthcare investment symposium in the industry, which connects global industry leaders, emerging fast-growth companies, innovative technology creators, and members of the investment community. For one week in January, from January 12^th to 15^th 2026, the health innovation universe converges in San Francisco.

In contrast to the CNS Summit’s intimacy, JPMorgan thrives on scale. It is where biotech, pharma and health system hopefuls present their value proposition to investors, and where global biopharma companies reaffirm strategic direction. From hotel lobbies to sidewalk cafés, every table and hallway becomes a “pitch” space. Deals are initiated, relationships rekindled, and reputations built, rebuilt or crushed. Mega consulting groups such as McKinsey emphasize that this gathering unites “global health and life sciences industry leaders, emerging fast-growth companies, innovative technology creators, and members of the investment community.”

You might attend JPMorgan for 72 hours and never set foot in an official session. Yet those unscripted encounters, coffee chats, quick handshakes, five-minute updates, often shape company trajectories and careers alike for the coming year.

Fern Lazar, Managing Partner and Global Health Practice Lead at FINN Partners, has attended the J.P. Morgan Healthcare Conference since its early days as the original Hambrecht & Quist Healthcare Conference—later acquired by J.P. Morgan. Her advice is straightforward: “Preparation is power. The companies that arrive with clarity, confidence, and proof of momentum leave with stronger reputations, investor trust, and new alliances. Those that don’t are quickly forgotten.”

Communication Tip: At JPMorgan, clarity is currency. In a sea of sound bites, those who communicate with precision rise above the noise. Be concise, compelling, and credible. Articulate what your company does, and why it matters to patients, to systems, and to investors. Every sentence should connect back to the vision and value with plenty of proof points to show you’re on the right track.

Why Both Matter to the Future of Health

Both conferences reveal something fundamental about the health industry’s DNA: innovation depends on both connection and coopetition. CNS Summit reminds us that science is human, built on relationships of trust. JP Morgan reminds us that sustainability requires strategy, clarity and capital.

For communication leaders, the lesson is clear: health innovation demands head, heart and gut.  You must speak to investors in the language of returns and to peers and partners in the language of purpose. The best communicators, like the best leaders, bridge both worlds seamlessly.

The life sciences sector is undergoing constant ebbs and flows: AI, digital biomarkers, decentralized trials and real-world data are reshaping how therapies are discovered, developed and delivered amid this race to raise the bar on all aspects of health access, affordability and delivery, leadership visibility and stakeholder trust matter more than ever.

At the CNS Summit, you cultivate the credibility that comes from empathy and engagement. The Summit community underscores that year-round social element: “Your event registration also provides access to our year-round programming and community activities.”  At JP Morgan, you demonstrate the confidence and messaging that attracts capital and partnership.

One builds influence, the other builds momentum. Taken together, they form a powerful narrative arc for any organization serious about advancing science and health innovation. The leaders who succeed in the next decade won’t just be the ones with great science, they will be those who can translate that science into stories that move payers, policymakers and patients alike. It’s the combination of IQ and EQ that will rally companies toward success.

Final Words: Show Up, Listen, Learn and Lead

Conferences are catalysts for connection.  Their value lies in what happens after the panels end and the flights home begin. Do you follow up? Do you stay in touch? Do you turn introductions into impact?

Whether you’re heading to Boston or San Francisco or both remember: your presence is an investment in the future of your career, company and community. Be intentional. Be visible. Most importantly, be human. In this ecosystem of change, as in medicine itself, the most significant advances begin when people listen, learn and lead together.

The post Two Gatherings, One Mission: Elevating Life Science Leadership and Communication appeared first on Medika Life.

He is a second-generation physician who prefers to be called by his first name, honoring his father, “the original Dr. Grossmann.”  In his own right, he’s a trailblazer at the nexus of surgical care and innovation. Born in Caracas, Venezuela and carrying forward his family’s medical legacy, he completed his surgical residency in Ann Arbor, Michigan, before establishing his practice in New England, serving as a general, trauma, advanced laparoscopic, and robotic surgeon at Portsmouth Regional Hospital in New Hampshire and Eastern Maine Medical Center.

Rafael is frequently linked to his groundbreaking use of Google Glass during surgery. But to define him by that singular innovation is to miss the deeper force driving his work: an unwavering belief that technology must serve—not supplant—the doctor–patient relationship. In recent interviews and longstanding contributions across digital health platforms, Rafael shares an increasingly urgent message: humility and empathy are not soft skills of the past—they are foundational elements of the future.

Proximity Over Performance
Rafael’s approach to technology is both deliberate and human-centered. He integrates AI, extended reality, and telehealth into care environments with one goal: to foster proximity between healer and patient. Whether bringing loved ones into ICU rooms through virtual tools, using augmented reality to teach medical trainees, or deploying wearables to enhance surgical insight, his purpose is consistent: technology must deepen the human connection.

“If the technology doesn’t enhance the connection between physician and patient,” Dr. Grossmann notes, “it has no role in care.”

That conviction reflects a broader truth in modern medicine: innovation must be guided by intention. The impact of a new tool is not measured by its complexity, but by its capacity to sharpen listening, expand compassion, and build trust. In this view, humility is not an abstract virtue—it is a clinical competency.

Humility as a Clinical Skill
While empathy is increasingly recognized as a measurable component of quality care, humility remains underappreciated. Yet humility—the ability to acknowledge limits, listen fully, and elevate the patient’s needs—may be one of the most critical skills a clinician can develop.

Rafael challenges medical education to do more than train for outcomes; he calls for cultivating presence. In trauma settings and academic halls alike, he models humility not as passivity, but as active, intentional leadership. It takes courage, he says, to be honest with patients—not just about diagnoses, but about uncertainty.

“The best medicine,” he reflects, “comes from presence, not only performance.” In high-tech environments where algorithms analyze and recommend, the clinician’s humility may be the most human—and healing—intervention available.

Empathy, Elevated by Innovation
To Rafael, empathy and innovation are not opposites. When used wisely, technology can extend—not replace—the clinician’s presence. Telemedicine platforms become conduits for comfort. Immersive simulations train for compassion. Data becomes dialogue when interpreted with care.

This mindset is especially important now. Patients today may have unprecedented access to information, yet they often feel unseen. In an age of instant answers, the experience of being truly heard remains rare. Rafael reminds health-sector leaders and policymakers that no system—however advanced—can succeed if it forgets the people it was designed to serve.

Clinicians stand at a crossroads as health delivery accelerates toward predictive analytics and AI-driven decisions. Technology offers an undeniable opportunity: greater access, improved accuracy, and better outcomes. But these advances must be matched by a return to the timeless principles of great medicine—empathy, humility, and presence.

Rafael’s work represents a rare blend of innovation and introspection. His willingness to explore the boundaries of digital medicine is matched by a steadfast insistence that patients remain at the center. The future of care, he contends, won’t be defined by who uses the most sophisticated technology, but by who uses it to deepen human connection.

Rafael is not focused on being remembered for the tools he introduced. He hopes to be known for something quieter: helping patients and clinicians feel seen, heard, and supported.

In an era when health systems are rethinking priorities, medical schools are reassessing competencies, and companies are racing to redefine care delivery, the voices of clinicians like Rafael’s matter more than ever. Humility, after all, is not the opposite of expertise—it is its most authentic expression.

The post “Humility” Is Cutting-Edge Medicine: What a Physician Innovator Teaches Us About Patient-Centered Care appeared first on Medika Life.

Artificial Intelligence (AI) is rapidly reshaping public health — from enhancing disease surveillance and diagnostics to easing workforce burdens — but it also raises complex risks and ethical questions. In Europe and globally, public health leaders are grappling with how best to harness AI’s revolutionary potential while managing its pitfalls. After decades of experience, many recognise that AI is not a magic fix for health challenges; its value depends on thoughtful integration into health systems. This article provides an in-depth review of the current relationship between AI and public health. It examines the opportunities it offers, real-world innovations already underway, practical implementation challenges, and the risks and governance frameworks that must guide responsible use. All discussions equally consider European contexts (including emerging EU regulations) and broader global health perspectives.

TL;DR Summary

• AI’s growing role in health: Artificial intelligence is increasingly used to augment public health efforts — from automating administrative tasks to advanced disease surveillance and diagnostics — offering new ways to improve efficiency and reach.
• Tangible benefits observed: Early deployments show promising results. AI tools have reduced clinicians’ paperwork burden, flagged outbreaks days before traditional systems, and enhanced diagnosis in low-resource settings (e.g. catching 15% more TB cases via X-ray analysis).
• Innovations across sectors: NGOs, governments, and companies are all investing in AI for health. For example, PATH and others use AI in field programmes, the NHS has dozens of AI pilots improving care delivery, and pharma companies leverage AI to speed up drug and vaccine development.
• Practical hurdles remain: Successful implementation requires robust data infrastructure, interoperability, and high-quality data. Many health systems must modernise IT systems and address data silos and quality issues before AI can perform optimally.
• Human factors are critical: Integrating AI into workflows and gaining staff acceptance are significant challenges. Training health workers, providing explainable outputs, and maintaining human oversight are essential to building trust in AI-assisted care.
• Key risks to manage: AI in public health brings serious risks — privacy breaches, algorithmic bias harming disadvantaged groups, opaque “black box” decisions undermining trust, and AI-generated misinformation spreading false health advice. Over-reliance on AI without safeguards can also be dangerous.
• Ethics and governance frameworks: Clear principles and regulations are emerging to guide responsible AI use. WHO’s six ethical principles (e.g. transparency, equity, accountability) set value-based guardrails, while the EU’s AI Act will enforce strict requirements on high-risk health AI (mandating transparency, risk management, and human oversight).
• Collaboration and capacity-building: Effectively advancing AI in public health will require interdisciplinary collaboration (health experts with technologists), investment in workforce AI literacy, and inclusive approaches that involve LMICs and marginalised groups so benefits are shared widely.
• Continuous evaluation and adaptation: To ensure AI delivers on its promise, public health authorities must continually monitor outcomes, audit algorithms for bias or errors, and be ready to adjust or suspend systems if problems arise. Adaptive governance and ongoing community feedback are vital for safe, effective AI integration.
• Seizing the opportunity responsibly: When guided by ethical principles and strong oversight, AI can greatly strengthen public health, easing workforce burdens, expanding outreach, and providing data-driven insights. The next few years are crucial for implementing the policies, education, and trust-building measures that will allow AI to be a force for health equity and innovation rather than a source of new disparities or dangers.

Opportunities: Transforming Public Health with AI

AI is being deployed to alleviate several longstanding public health challenges. One significant opportunity is reducing clinician burnout and workforce shortages by automating routine tasks. For example, a 2024 survey found that 57% of physicians believe automating administrative burdens is the top opportunity for AI to ease workloads amid staff shortages. Machine learning systems can transcribe medical notes, pull up patient records, and handle scheduling or prescription refills — freeing clinicians to spend more time on patient care. Many doctors see such automation as a key to improving work efficiency and reducing stress, suggesting AI could help mitigate the healthcare burnout epidemic.

AI also offers powerful tools for disease surveillance and epidemic intelligence. Algorithms can continuously scan vast data sources — news reports, social media, travel data — to spot early signs of outbreaks far faster than traditional methods. Notably, the HealthMap and BlueDot platforms (which use natural language processing and machine learning) flagged the COVID-19 outbreak days before official alerts. By sifting through informal signals and anomalies, AI-driven systems can provide precious early warnings of emerging health threats. BlueDot’s AI surveillance tools have dramatically sped up outbreak detection, reducing manual scanning time by nearly 90% in some cases. Such early alerts enable public health agencies to mobilise quicker responses and potentially contain outbreaks before they spread.

Another area of opportunity is improving diagnostics and clinical decision support, especially in resource-constrained settings. AI image recognition has shown great promise in interpreting medical images like X-rays and retinal scans. For example, AI-based chest X-ray tools for tuberculosis (TB) are being used to help screen patients in low-resource areas that lack radiologists. A recent programme in India led by PATH found that an AI tool (qXR) boosted TB case detection by ~15.8% — identifying cases that human readers missed. Many countries are now utilising AI-assisted chest X-ray screening for TB, which can lead to earlier diagnosis and treatment in underserved communities. Beyond imaging, AI-powered diagnostic apps and chatbots can guide patients through symptom checks or flag high-risk cases for follow-up, expanding access to essential healthcare advice where clinicians are scarce.

Crucially, AI is also being enlisted to address climate-related health threats and environmental impacts on health. Public health researchers increasingly pair AI with climate data to predict disease patterns under changing environmental conditions. For instance, machine learning models can correlate weather patterns (temperature, rainfall) and even animal health data with disease outbreaks to anticipate risks in specific locations. By analysing such data, AI-driven predictive analytics can serve as early warning systems — forecasting surges in vector-borne diseases like malaria following heavy rains or heat-related illness during extreme heatwaves. This capability is ever more critical as climate change intensifies health hazards. AI can help public health officials prepare for climate-sensitive disease outbreaks, allocate resources proactively, and develop adaptation strategies to protect vulnerable populations.

Real-world Applications and Innovations

AI in public health is not just theoretical — numerous real-world initiatives by NGOs, governments, and private companies have already demonstrated its potential. Global health nonprofits and international agencies have been early adopters of AI to support their missions. For example, the Bill & Melinda Gates Foundation has invested heavily in AI-driven global health projects. In 2023, it awarded grants to nearly 50 pilot projects exploring AI solutions for health and development challenges — these range from AI-augmented diagnostic tools to data systems for disease surveillance in low-income settings.

One Gates-backed innovation is AI-assisted ultrasound: in 2020, a $44 million grant was given to develop an AI-guided portable ultrasound to improve lung disease diagnosis in low-resource countries (e.g. detecting pneumonia). Likewise, PATH and other NGOs are integrating AI into field programmes — as seen in the TB screening project, where an AI tool significantly increased case finding while illuminating practical deployment hurdles. These efforts by NGOs underscore AI’s promise to close gaps in healthcare access and quality for underserved populations.

Governments and public health agencies are also launching AI initiatives. In Europe, national health systems pilot AI to improve services and efficiency. For instance, the UK’s National Health Service (NHS) created an NHS AI Lab to fund and evaluate AI innovations in care delivery. By 2025, the NHS had over 80 AI projects live, targeting everything from optimising nurse rostering and predicting hospital bed occupancy to speeding up radiology workflows.

One NHS program provided £100+ million in awards to develop AI for earlier cancer detection, resource management, and patient safety improvements. The NHS AI Lab’s “Skunkworks” team has run short-term projects that yielded practical tools — e.g. an algorithm to streamline the placement of nurses across wards and a natural language processing engine to search health records more efficiently. Meanwhile, European public health agencies are leveraging AI for epidemiology; the European Centre for Disease Prevention and Control (ECDC) has incorporated systems like BlueDot’s AI to enhance epidemic intelligence, including monitoring outbreaks during events such as the 2020 Olympics. These government-led efforts illustrate growing public sector commitment to deploying AI for health system strengthening and emergency preparedness.

The private sector, particularly in healthcare and pharmaceuticals, is likewise driving innovation at the intersection of AI and public health. Pharmaceutical companies now routinely use AI in drug discovery and development. For example, Novartis recently struck a wide-ranging partnership (worth up to $1 billion) to use a generative AI platform for designing new protein-based therapies — aiming to accelerate the search for novel disease treatments. GSK has also embraced AI to speed up R&D: its CEO noted that AI modelling helped cut two years off an RSV vaccine trial by predicting where outbreaks would occur and optimising trial site selection. This led to the faster development of the world’s first RSV vaccine, an essential public health breakthrough.

Beyond pharma, medical technology firms are integrating AI into devices, from smart wearables that flag irregular heart rhythms to imaging systems where AI assists in analysing scans for early signs of cancer. Startups and tech companies are introducing AI-driven health apps and chatbots (such as symptom checkers and mental health conversational agents), which some health services in Europe are trialling for patient triage and support. These real-world examples underscore that AI is already deeply enmeshed in the health ecosystem — from global disease surveillance networks to hospital wards and R&D labs — delivering innovations that could improve population health outcomes.

Practicalities and Implementation Challenges

While the potential is immense, implementing AI in public health is a pragmatic challenge. Infrastructure and data interoperability are foundational hurdles. Effective AI requires robust digital infrastructure — high-quality data streams, electronic health records, and cloud computing capacity — which many health systems lack, especially in low-resource settings. Data needed for public health AI often reside in silos or incompatible formats across hospitals, labs, and agencies. Poor interoperability means AI tools struggle to aggregate and interpret information from disparate sources. Bridging these gaps will require significant investment in health information systems, common data standards, and connectivity. Encouragingly, current AI technology can assist in standardising and mapping messy health datasets to make them more usable. Nonetheless, without reliable infrastructure and data-sharing frameworks, even the best AI algorithms cannot deliver consistent results across a public health network.

A related challenge is data quality and representativeness. AI models are only as good as the data they learn from, and health data can be incomplete, biased, or unrepresentative of specific populations. Studies highlight issues like variability in how data are recorded, large amounts of unstructured text, missing information, and coverage bias (e.g. most training data coming from high-income populations).

These factors can undermine an AI system’s accuracy and value to end users. Developing good AI for health requires carefully cleaning and curating data to reflect clinical reality. For instance, algorithms trained only on European hospital data may perform poorly in rural African communities. Implementers must thus invest effort in data preparation and continuously monitor model outputs for anomalies. Establishing metadata standards, common terminologies, and data quality metrics can facilitate better AI development. Additionally, clarity on data ownership and governance is needed: questions about who “owns” health data (patients, providers, governments?) affect how data can be integrated for AI. Resolving these issues through policies and trust frameworks is key to unlocking data for public health AI while respecting privacy and rights.

Another practical consideration is integrating AI tools into healthcare workflows and gaining workforce acceptance. Introducing AI decision-support systems or automation in clinics requires adapting processes and training staff. Health workers may be understandably cautious — some lack familiarity with AI, worry about accuracy, or fear being displaced. Clear protocols are needed if an AI system’s recommendation conflicts with clinical judgment. Early experience shows that human-AI collaboration works best when AI is framed as an assistive tool rather than a professional replacement. Building trust among the workforce involves providing explainable outputs and demonstrating reliability in pilot phases. It also means training clinicians in basic AI concepts and ensuring they feel confident interpreting AI outputs.

Successful deployments (like the PATH TB screening program) emphasise that significant workflow integration and training efforts are required. In that program, implementers had to solve issues of installing the software in clinics, securing internet connectivity for the AI, and ensuring staff could effectively use the AI results within their screening workflow. Without such groundwork, even a high-performing algorithm might sit on the shelf unused. Thus, the human element is crucial: public health organisations must engage and educate their workforce, adjusting roles and processes so that AI enhances rather than disrupts care delivery. Over time, as clinicians see AI reducing drudgery (e.g. auto-filling forms) and improving outcomes, their acceptance tends to grow. Indeed, physician enthusiasm for health AI has been rising year-on-year. Patience and iterative refinement are needed to blend AI smoothly into the complex fabric of health systems.

Risks and Concerns of AI in Public Health

Despite the optimism, it is vital to acknowledge the risks and potential harms associated with AI in public health. Data privacy and security tops the list of concerns. AI systems often require large datasets of patient information, raising the stakes for protecting sensitive personal health data. Any breach or misuse of such data can erode public trust and violate individuals’ rights. There is also the risk of “function creep”, where data collected for health purposes might be used in other ways (for example, a COVID-19 contact tracing app’s data later being used for law enforcement — a scenario that drew criticism in some countries). Moreover, complex AI models could inadvertently leak private details — for instance, a model might be reverse-engineered to reveal records it was trained on. Ensuring robust cybersecurity and strict data governance is therefore paramount. Many call for comprehensive privacy safeguards and compliance with regulations like Europe’s GDPR whenever AI handles health data. Techniques such as anonymisation or synthetic data can help, but they are not foolproof (even de-identified data can sometimes be unidentified).

The bottom line: without public confidence that AI will maintain confidentiality and data security, its benefits will be lost. Public health agencies must be transparent about what data are used and how to obtain informed consent where appropriate and implement state-of-the-art security measures to prevent breaches. Privacy isn’t just a legal box to tick — it’s fundamental to preserving the trust on which public health interventions depend.

Another significant risk is algorithmic bias and the exacerbation of health inequalities. AI systems can unintentionally perpetuate or even worsen disparities if their design is not carefully managed. This was starkly illustrated by a widely used healthcare risk algorithm in the United States that was found to be racially biased. The algorithm helped determine access to extra care programs and used healthcare cost as a proxy for need. This choice systematically underestimated the needs of Black patients (who often had lower healthcare expenditures due to access barriers). As a result, many high-risk Black patients were less likely to be flagged for additional care, denying them the resources they needed. This example shows how bias in data or design can translate into inequitable outcomes: the AI effectively discriminates against a vulnerable group. Similar issues could arise in public health if an AI model is trained on predominantly male patients under-detect conditions in women or if disease surveillance AI better covers wealthier communities with more data. AI could widen gaps if not addressed, with marginalised populations benefiting the least or even being harmed.

Equity must be a central design principle to counter this: datasets should be diverse and inclusive, algorithms should be tested for bias, and bias mitigation strategies (like reweighing data or algorithmic fairness adjustments) should be applied. The WHO explicitly highlights inclusiveness and equity as core ethical principles for AI, ensuring that AI tools work for all segments of society regardless of race, gender, income, or other characteristics. Ultimately, careful governance and auditing of AI systems are needed to avoid encoding systemic biases into digital form and instead use AI to reduce health inequities (for example, by targeting interventions to underserved areas).

A further concern is the lack of transparency (“black box” issue) and its impact on trust and safety. Many AI models, especially deep learning networks, operate as complex black boxes — they do not explain their reasoning in human-understandable terms. In healthcare, this opacity is problematic. Clinicians and public health decision-makers are wary of acting based on a recommendation they don’t understand, particularly if an AI’s advice contradicts intuition or standard practice. Unexplainable AI can also undermine accountability: if an AI makes a harmful mistake, it may be unclear why it happened or who is responsible. This lack of transparency feeds directly into trust issues among professionals and the public. If people perceive AI as a mysterious, untrustworthy “magic wand” imposed on health decisions, they may reject its use. There have been cautionary tales: an AI system deployed in hospitals to predict which COVID-19 patients would need ICU care was later found to underperform because it hadn’t been adequately validated. Clinicians grew sceptical of its risk scores.

To prevent such scenarios, experts call for explainable and interpretable AI in health — algorithms that can provide reasons for their predictions or use transparent, logical rules where possible. At a minimum, users should have access to information about how an AI was developed and its known limitations. Regulatory frameworks like the EU AI Act are likely to mandate a degree of transparency for high-risk AI (including many medical applications) precisely to bolster trust and enable oversight. Building more explainability into AI models remains a technical challenge, but one that is essential for aligning with the principles of transparency and accountability in healthcare.

In the age of ChatGPT and generative AI, misinformation and “AI hallucinations” have emerged as new public health risks. Advanced chatbots can produce remarkably human-like answers to questions — but they do not guarantee factual accuracy. They can hallucinate false information, confidently output incorrect medical advice, nonexistent statistics, or even fake health news. The potential for harm is considerable if the public uses such tools for health information. There is concern that AI chatbots could magnify the health misinformation problem exponentially — for instance, by generating convincing anti-vaccine narratives or spurious cures, which then spread on social media.

In recent years, public health agencies have struggled to combat misinformation (for example, false claims about vaccines or COVID-19 treatments that undermine uptake). The rise of AI-driven content generators and deepfakes only fuels this fire. Misinformation undermines public trust and can lead people to reject proven interventions in favour of dangerous alternatives. Tackling this will require new strategies — such as watermarking AI-generated content, strengthening content moderation, and improving digital health literacy so the public can better discern credible information. On the flip side, public health communicators might also leverage AI to fight misinformation (for example, using AI to detect false rumours early or personalise accurate health messages). Regardless, the advent of easy, AI-generated disinformation is a serious risk factor that the global health community cannot ignore.

Finally, there is the risk of over-reliance and systemic dependency on AI. If health systems come to depend on AI for critical functions without adequate safeguards, any failures in the technology could have severe consequences. For example, an AI model might perform well in normal conditions but fail to generalise during an unexpected scenario. If everyone has come to rely on its output, they may miss the warning signs until too late. Moreover, heavy reliance on automation might erode human skills over time (a phenomenon observed in other industries). In healthcare, this raises concerns about “deskilling” — clinicians might lose practice in specific tasks (like reading x-rays or making complex diagnoses) if those are always handled by AI, leaving them less prepared to step in when needed.

Over-reliance can also dull vigilance: users might stop double-checking results if an algorithm usually works well so that an undetected error could propagate. The key is to maintain a human-in-the-loop approach: AI should support, not replace, human expertise. Mechanisms for human review of AI outputs and fallback plans in case of system outages are essential.

Additionally, performing regular audits and updates of AI models can prevent performance from degrading unnoticed. In summary, while AI can increase efficiency, public health systems must guard against blindly relying on algorithms. A balanced approach that values human judgment and institutional memory, alongside AI’s computational power, will be safest in the long run.

Ethical and Regulatory Frameworks

Addressing the above risks requires robust ethical guidelines and regulatory oversight for AI in health. Globally, there is growing consensus on core ethical principles that should govern AI development and use in public health. The World Health Organization’s landmark 2021 report laid out six guiding principles for ethical AI in health: (1) Protect human autonomy — humans should remain in control of health decisions, with informed consent and respect for privacy; (2) Promote human well-being and safety — AI must be safe, effective, and designed to improve health outcomes; (3) Ensure transparency, explainability and intelligibility — stakeholders should have sufficient information about how AI systems work and decisions should be traceable; (4) Foster responsibility and accountability — developers and users are accountable for AI behaviour, and mechanisms for redress must exist; (5) Ensure inclusiveness and equity — AI should benefit all groups, enhancing fairness and not amplifying disparities; and (6) Promote AI that is responsive and sustainable — meaning AI should be adaptable, monitored, and designed for long-term societal benefit.

These principles, while high-level, provide a value framework to guide everything from design choices (e.g. using diverse training data to ensure equity) to deployment (e.g. always keeping a human in the loop to protect autonomy). Public health organisations are increasingly adopting such ethical frameworks. For instance, the WHO urges that AI deployments be accompanied by community engagement, training for health workers, and continuous evaluation to ensure technologies remain aligned with the public interest. The ethos is straightforward: AI must be people-centred and uphold human rights. Ethics committees or advisory boards can help oversee AI projects, reviewing them for compliance with these principles before they scale up.

On the regulatory front, governments are now moving to establish formal rules for AI in healthcare. The European Union’s AI Act is a pioneering example of comprehensive regulation. Passed in 2024, the EU AI Act takes a risk-based approach, classifying AI systems by risk level and imposing requirements accordingly. Health-related AI is generally deemed “high-risk” under this law, given its potential impact on people’s lives and rights. High-risk AI systems (including most AI used for medical diagnostics, decision support, or resource allocation in health) will face strict obligations. These include rigorous standards for transparency, risk management, and human oversight. For instance, developers of a clinical AI tool must implement a quality management system, ensure their model is trained on appropriate data, and provide documentation detailing the AI’s function and limitations. They must also conduct risk assessments and put in place human oversight measures to prevent automation bias. Notably, the EU AI Act doesn’t just apply to creators of AI — it also holds deployers (such as hospitals or public health agencies) accountable for the safe use of AI.

Health providers must monitor AI system performance, keep logs, and retain ultimate responsibility for decisions (clinicians must have the authority to override AI recommendations if needed). These provisions aim to ensure that human accountability and patient safety remain paramount even as AI becomes embedded in care delivery. Additionally, the Act has a broad reach: any AI system impacting people in Europe must comply, even if developed elsewhere. This could set an effective global benchmark as companies worldwide adjust their practices to meet the EU’s requirements.

Other jurisdictions are also crafting guidelines. The United States, through the FDA, has been evolving its regulatory approach for AI/ML-based medical devices, focusing on premarket evaluation and the idea of “continuously learning” algorithms needing ongoing monitoring. International bodies like the WHO have issued guidance and urged governance innovation, suggesting that governments update regulations to cover AI, establish certification processes, and possibly create registries of approved AI health products. We also see emerging governance models such as algorithmic impact assessments (to evaluate a health AI system’s potential societal impact before deployment) and independent reviewers’ bias audits. In some health systems, procurement of AI now requires meeting ethical checklists or obtaining approval from institutional review boards, similar to new medical interventions.

These steps are part of building a “responsible innovation” culture around AI, encouraging experimentation and advancement, but within guardrails that protect individuals and communities. Multi-stakeholder collaboration is key here — regulators, technologists, health professionals, and patient representatives need to work together to define safe and effective AI in practice and update those definitions as the technology evolves. As one example, the NHS AI Lab in the UK partnered with regulators to create a sandbox for AI developers, guiding them on navigating regulatory pathways and using synthetic data for testing. Such efforts show that with thoughtful governance, innovation and safety can advance hand in hand.

Future Directions and Recommendations

To fully realise AI’s promise in public health while minimising its downsides, several changes and strategic efforts are needed going forward:

• Investing in data and digital infrastructure: Health systems, especially in low- and middle-income countries, need support to build the data foundations for AI. This means digitising health records, improving data quality, and ensuring platform interoperability. Governments and global donors should prioritise funding for health information systems and broadband connectivity as part of public health capacity building. Better data infrastructure not only enables AI — it strengthens health systems overall. Innovative approaches like federated learning (where AI models train on distributed data without moving it) could be scaled to allow resource-constrained regions to benefit from AI insights without breaching privacy. The goal is to create a world where data flows securely and efficiently to wherever it can improve health outcomes.
• Strengthening workforce capacity and AI literacy: As AI becomes a standard tool, public health and healthcare workers must be equipped to use and oversee it. Training programmes are needed to raise AI literacy among the health workforce, including understanding AI’s capabilities and limitations. This may involve updating medical and public health curricula to cover data science basics. Additionally, new specialist roles (such as clinical AI safety officers or epidemiologists with AI expertise) could be developed to bridge the gap between tech and health domains. Frontline staff should be engaged in co-designing AI solutions so that tools are user-friendly and address actual pain points. When health workers understand and trust AI, they can become champions for its adoption and serve as critical watchdogs who notice when something isn’t right. Fostering a culture of continuous human oversight and feedback will ensure that AI remains a servant to health professionals, not a black box dictator.
• Ensuring inclusivity and equity in AI advancement: The global health community must actively work to prevent a digital divide in AI. Much cutting-edge AI development is concentrated in wealthier countries and tech companies. Deliberate efforts are needed to include researchers and perspectives from low- and middle-income countries in AI design so that solutions address diverse needs. This could consist of research funding earmarked for LMIC-led AI projects, technology transfer programs, and south-south collaboration on AI for health. Moreover, data from underrepresented populations should be collected (with consent and protection) to improve algorithms’ relevance in those settings. By democratising AI knowledge and resources, we can avoid a scenario where only certain countries or communities benefit from AI while others are left behind or subject to unchecked harm. Equity considerations should also extend to gender, age, and other demographics — for instance, ensuring women and minority groups are included in AI development teams and that tools serve users of different languages and literacy levels. An inclusive approach will make AI tools fairer and enlarge the talent pool working on creative AI solutions for entrenched public health challenges.
• Fostering collaboration between public health and technology sectors: Effective AI in public health sits at the intersection of epidemiology, medicine, data science, and engineering. No single sector can do it alone. We need stronger partnerships: governments linking with academia and tech firms, NGOs working with startups, and international agencies convening multi-sector consortia for global health AI initiatives. Such collaboration can accelerate innovation and ensure that public health priorities guide technological development (and vice versa, that technologists are aware of on-the-ground needs). For example, a partnership between a national health ministry and AI researchers might focus on building an early warning system for malaria outbreaks, combining epidemiological expertise with cutting-edge modelling. A pharmaceutical company could also collaborate with global health organisations to use AI in vaccine R&D for diseases of poverty. These cross-sector collaborations should be underpinned by fair agreements (e.g. around data sharing or intellectual property) so that all parties benefit and trust is maintained. The complexity of health + AI demands breaking down silos. International forums and networks can play a role here, enabling countries to share best practices and lessons learned (e.g. how one country successfully regulated an AI symptom-checker or how another trained health workers on AI). Since pathogens do not respect borders, a collaborative global approach to AI-enhanced public health security is in everyone’s interest.
• Adaptive governance and continuous evaluation: As AI tools roll out, it is critical to monitor their real-world impact and be ready to adjust course. Public health authorities should implement mechanisms to continuously evaluate AI interventions — collecting data on their accuracy, outcomes, and any unintended effects. Are the predictions helping improve disease control? Is a triage algorithm safely directing patients to the right level of care? This requires establishing key performance indicators and perhaps creating independent evaluation units. When problems are identified (such as an AI starting to drift in accuracy due to changes in data), there should be processes to update or pull back the tool until fixes are in place. Regulation must also remain adaptive; rigid rules could stifle innovation or become outdated as technology advances. One idea is regulatory sandboxes where new AI solutions can be tested under supervision, allowing regulators to learn and guidelines to evolve. Governance models should be proactive yet flexible, emphasising learning and iteration. Importantly, communities and civil society should have a voice in evaluating AI in public health — their feedback on whether these tools are culturally acceptable, understandable, and improving services is invaluable. Responsible AI is not a one-time certification but an ongoing commitment to quality and ethics throughout the technology’s lifecycle.

Looking ahead, it is clear that AI will play an expanding role in public health — whether in combating the next pandemic, extending healthcare to remote villages via smart apps, or analysing big data to pinpoint disease drivers. The revolution is already underway, but its trajectory depends on our current choices. With enlightened leadership, adequate safeguards, and inclusive collaboration, AI could usher in significant public health gains — from more efficient health systems to healthier communities worldwide. However, if we ignore the risks — allowing unchecked use, widening inequities, or losing the human touch in care — the potential benefits could unravel, and public trust could be irrevocably lost. The coming years are thus pivotal. Armed with decades of hard-won experience, public health professionals have a key role in steering this journey. By insisting on evidence, equity, transparency, and community engagement, they can ensure that the AI revolution in health truly becomes a boon and not a threat. The opportunity is immense, but so is the responsibility to guide AI’s integration into public health thoughtfully and ethically.

The post AI in Public Health: Revolution, Risk and Opportunity appeared first on Medika Life.

According to the report, global investment in Consumer Health Technology grew by 9% in 2024, totaling $4.5 billion. This increase reflects investor confidence in technology-driven health solutions and signals a shift toward consumer empowerment. However, despite the surge in funding, the report underscores persistent challenges, including a clinical validation gap that could hinder long-term adoption and trust.

“The Consumer HealthTech sector is at a pivotal moment,” said Julien de Salaberry, founder and CEO of Galen Growth. “We are witnessing the continued health-sector evolution where consumers seek to secure stronger ownership over their health and well-being. People who succeed in using their buying power to secure innovation with clinical validation will shape the future of health.”

Investment and Market Dynamics

Galen Growth’s report reveals a complex funding environment where record investment is contrasted by uneven access to capital. While total investment rose to $4.5 billion in 2024, the market remains heavily weighted toward early-stage ventures:

• 75% of Consumer HealthTech ventures are still in the seed stage, highlighting the challenge of securing Series A and beyond.
• Only 25% of ventures incorporated in the last seven years have progressed to Series A or beyond.
• 11 mega deals (valued at over $100 million) accounted for 37% of overall funding, reflecting investor appetite for mature ventures with proven models.

Regional Investment Trends

North America remains the dominant market, capturing 64% of total funding and securing 78% of global venture capital deals. The region’s leadership in funding reflects its mature digital health ecosystem and strong investor appetite for high-growth, technology-driven health solutions.

Europe attracted 24% of global funding, focusing on corporate health and wellness solutions. In contrast, the Asia-Pacific region secured 12% of international funding, driven by increased investment in preventive health marketplaces and telemedicine.

Notable Deals from 2024:

• Neko Health secured a $260M Series B for its AI-driven preventive health model, which is focused on early detection and personalized treatment recommendations.
• eGym, a global fitness technology leader, raised $201M in a Series H to expand its connected training platform and enhance its AI-powered coaching system.
• Spring Health, a mental health platform, raised $100M in a Series E to scale its personalized therapy and mental wellness offerings.
• Flo Health, a women’s health app focused on menstrual cycle and fertility tracking, raised $200M in a Series C to expand its product offerings and global reach.

Mental Health Takes the Lead

Mental health emerged as the dominant focus in Consumer HealthTech in 2024, attracting 29% of total funding—underscoring the growing recognition of mental well-being as a vital part of overall health.

“Consumer HealthTech has the power to democratize health by giving individuals greater control over their health,” added de Salaberry. “But for this potential to be realized, ventures need to address the division between the haves and have-nots and the clinical evidence gap to demonstrate measurable health outcomes.”

The rise in mental health investment reflects a shift toward more personalized, technology-driven care models. Consumers increasingly expect the same level of convenience and personalization in health that they experience in other industries, such as retail and finance. This demand has spurred innovation in teletherapy, mental health apps, and AI-driven cognitive behavioral therapy.

Key Therapeutic Areas:

• Women’s Health saw a surge in funding, capturing 20% of total investment. Startups like Flo Health are capitalizing on the growing demand for reproductive health and fertility tracking.
• Preventive Health attracted 17% of funding, driven by the rise of AI-powered diagnostics and real-time health monitoring.
• Sleep and Cognitive Health solutions are also gaining traction as consumers seek to improve overall well-being through better sleep quality and cognitive enhancement.

Strategic Partnerships and M&A Activity

Partnerships and consolidation play a critical role in the sector’s growth:

• More than 900 new partnerships were formed in 2024, signaling growing collaboration across the health ecosystem.
• Venture-to-venture acquisitions accounted for 79% of M&A activity, highlighting how companies are using consolidation to strengthen market presence.
• Notable partnerships included:
  • Pfizer partnered with GoodRx to improve medication management and strengthen direct-to-consumer outreach.
  • Amazon expanded its mental health footprint through a strategic partnership with Talkspace for teleconsultation services.
  • Mayo Clinic partnered with Prenetics to advance genomics-based health solutions and expand access to precision medicine.

This trend toward consolidation reflects increasing pressure to scale solutions and navigate complex regulatory and reimbursement landscapes. More prominent players are looking to leverage established consumer brands and data platforms to enhance their competitive positioning.

Issues and Prospects

Despite the rise in funding and market activity, the Consumer HealthTech sector faces critical challenges:

• Clinical Validation Gap: The report highlights that Consumer HealthTech ventures exhibit an 11% lower clinical evidence signal than the broader digital health ecosystem. This gap raises concerns about scientific rigor and long-term adoption.
• Funding Bottleneck: Early-stage ventures struggle to secure Series A funding, with most capital concentrated in late-stage deals.
• Accessibility and Equity: Unlike traditional health, which is often covered by insurance, Consumer HealthTech solutions rely heavily on out-of-pocket spending. This creates a divide between those who can afford premium health services and those who cannot.

“The health sector cannot afford to become a system of haves and have-nots,” said de Salaberry. “Consumer HealthTech ventures must work to close the accessibility gap by developing affordable solutions and proving their clinical value.”

Future Trends and Market Outlook

The Galen Growth report identifies several key trends shaping the future of Consumer HealthTech:

• AI-Driven Personalized Medicine: AI-powered diagnostics and health assistants enable hyper-personalized treatment plans based on genetic, biometric, and behavioral data.
• Hybrid Models: Successful Consumer HealthTech ventures increasingly integrate with traditional health systems to improve credibility and adoption.
• Regulatory Oversight: Governments and regulators will likely increase scrutiny over health claims and data privacy, creating startup opportunities and challenges.
• Equity and Affordability: To achieve long-term impact, Consumer HealthTech solutions must address disparities in technology access and affordability.
• Digital Therapeutics: Prescription-based digital interventions for managing chronic conditions are poised to bridge the gap between technology and evidence-based care.

A Defining Moment for Consumer HealthTech

Consumer HealthTech represents a seismic shift in health—moving from reactive to proactive care. As Galen Growth’s report reveals, the future of health lies in empowering individuals with technology-driven solutions that provide real-time insights and personalized care.

The rise of AI-driven diagnostics, wearable technology, and telemedicine enables consumers to monitor and manage their health in real time. This shift toward preventive care improves outcomes and reduces the burden on traditional health systems.

However, sustainable growth requires stronger clinical validation and deeper integration with established health frameworks. Consumer HealthTech ventures must navigate the complexities of reimbursement, regulation, and market competition to secure long-term success.

The Galen Growth report makes it clear: The future of health belongs to those who empower consumers—and prove it works.

The post Consumer HealthTech: A Data-Driven Evolution in Health Engagement appeared first on Medika Life.

At the core of this transformation lies a new breed of companies—from patient engagement platforms and AI-powered diagnostics to cybersecurity solutions and social care integration—all focused on improving patient outcomes, enhancing institutional efficiency, and creating a more equitable health environment.

Over the past decade, many health provider systems have recognized that the key to improving health is not isolated solutions but integrating complementary technologies that enhance care delivery and operational performance. This shift in focus is evident in how providers and service companies are solving real-world problems, from improving workflows to using data analytics to enhance clinical decision-making and patient care.

Growth of the Health Innovation Pipeline

The health innovation pipeline has expanded significantly. The ecosystem has matured from focusing mainly on back-end technologies—such as EHR systems and clinical decision support tools—to encompassing forward-thinking solutions that put health professional effectiveness and patient experience at the center of the equation. Today, that pipeline includes:

1. Patient Experience Companies: These companies leverage digital health solutions such as smart hospital rooms, patient portals, and AI-powered scheduling tools to improve patient engagement and satisfaction. Companies like Vibe Health by eVideon demonstrate how patient experience can be enhanced with interactive technology, ensuring patients are informed and empowered to make decisions about their care.
2. Data-Driven Operational Improvements: Data analytics, artificial intelligence (AI), and machine learning enable health institutions to make informed decisions guided by curated information. Companies like CereCore and Tegria are at the forefront of transforming clinical operations and revenue cycle management through advanced data analytics, AI-driven insights, and cloud-based solutions. These tools help health systems optimize workflows, reduce administrative burdens, and enhance financial performance, all contributing to a more efficient system.
3. Patient Outcome-Focused Solutions: Improving patient outcomes must become the ultimate goal of health, and companies in this space are using innovative technologies to address social determinants of health (SDOH), chronic disease management, and real-time clinical decision support. Companies like Unite Us are addressing SDOH by connecting patients with community-based resources, ensuring that health providers can offer holistic care beyond medical treatment, including housing, food, and mental health services.

Health Innovation: From Reaction to Proactive

Historically, health systems often reacted to crises—whether a pandemic, financial strain, or workforce shortage—with patchwork solutions. Today, they are shifting from reactionary measures to proactive strategies. Companies seek transformational models and drive long-term sustainability through integration and automation.

For example, solutions from companies like Absolute Security and TigerConnect enable health institutions to proactively address cyber threats and disruptions in care and maintain system continuity despite unprecedented challenges. This level of preparedness and resilience enables health systems to focus on care delivery and data-driven patient management, ensuring that patient outcomes are improved and resources are optimally allocated.

Absolute Security: Ensuring Cyber Resilience in Health

As health systems become digitally interconnected, cybersecurity threats are escalating, particularly with the rise of ransomware and endpoint security breaches. Absolute Security addresses these vulnerabilities by embedding its patented Persistence Technology directly in the firmware of more than 600 million Windows devices, ensuring that security controls are maintained even when operating systems are compromised.

At HIMSS 2025, Absolute Security shared critical findings from telemetry data covering some million health PCs, highlighting a concerning 15 percent failure rate in security tests. These failures expose health organizations to increased risks and compliance issues. Absolute’s solutions offer unmatched resilience by enabling hospitals and clinics to recover devices to their original, fully operational state, regardless of whether cyber-attacks caused disruption, system crashes, or IT malfunctions.

Absolute can provide continuous protection and ensure quick recovery, which is particularly crucial for health organizations that rely on critical infrastructure. Their solutions keep security controls, applications, and operating systems up and running, providing secure access even in critical situations and ensuring that health teams can maintain focus on delivering patient care.

AvaSure: Revolutionizing Virtual Care with AI-Powered Technology

As hospitals and health systems continue to face mounting pressures to balance exceptional patient care with operational efficiency, AvaSure is stepping up with a game-changing solution.

 At HIMSS 2025, the company is launching its AI-powered Virtual Care Assistant, “Vicky,” developed in collaboration with Oracle Cloud Infrastructure (OCI) and NVIDIA. This cutting-edge technology bridges communication gaps, streamlines clinical workflows, and prioritizes urgent patient needs, improving patient care and operational efficiency. Vicky empowers healthcare teams to reduce response delays, alleviate clinician burdens, and improve patient outcomes. By integrating advanced AI into the care process, the AvaSure Virtual Care Assistant is a tool for streamlining tasks and a transformational solution reshaping how health providers operate and deliver care.

CereCore: Bridging Clinical and IT Divides

As health systems digitalize, improving clinicians’ EHR (Electronic Health Record) usability remains a central focus. CereCore, the IT arm of HCA Health, has been instrumental in bridging the gap between clinical and IT teams. At HIMSS 2025, CereCore showcased its approach to resolving long-standing EHR challenges by improving back-end IT support through peer-to-peer pairings and data analytics.

CereCore solutions streamline workflows, enhance system usability, and resolve real-time EHR issues, reducing downtime and increasing productivity. Their expertise spans some of the most prominent EHR systems, including Epic, Meditech, and Oracle Health (Cerner). CereCore provides comprehensive IT help desk services, cybersecurity, cloud transitions, and revenue cycle management support.

This company is helping health systems overcome some of the most pressing technology challenges by improving clinical and IT staff collaboration. For the people who are the frontline of healing, CereCore offers clinicians and patients a seamless and more efficient care experience.

TigerConnect: Streamlining Emergency Care Coordination

One of the most urgent needs in health today is improving information exchange between emergency medical services (EMS) and emergency departments (EDs). Traditional communication tools, such as radios and pagers, result in slow response. At HIMSS 2025, TigerConnect introduced its Pre-Hospital and Transfer solutions to streamline emergency care workflows and improve patient throughput.

TigerConnect Pre-Hospital solution enables EMS teams to send patient information to the ED on the way to the hospital. This helps teams to rally and prepare to transition from ambulance to in-patient care coordination. Their Transfer solution digitizes the process of patient transfers, reducing the number of manual phone calls and minimizing delays.

By leveraging real-time communication and automation, TigerConnect improves emergency care efficiency, reduces time-to-treatment, and enhances clinical decision-making. As health continues to adopt digital tools, TigerConnect solutions will play an integral role in improving care delivery across the emergency spectrum.

Tegria: Guiding Digital Transformation

As health systems evolve, guidance is essential for implementing new technologies. Tegria, with its team of 1,500 professionals, is playing a key role in helping health organizations navigate digital transformation and operational improvements – providing sector-specific depth, operational experience across hundreds of health systems, and much-needed objective counsel and hospital networks to make critical IT infrastructure investment decisions.

Founded by Providence, one of the largest non-profit health systems in the U.S., Tegria offers expertise in IT optimization, revenue cycle management, and cloud-based solutions. The company supports 650 health organizations across North America and Europe, helping them adopt emerging technologies that drive efficiencies, reduce costs, and improve care quality.

Tegria partnership with health organizations empowers them to embrace digital tools while improving their financial sustainability and clinical outcomes. The company’s continued value and success are reflected in its #1 rank as a leading health IT Consulting Service and a top performer in Clinical Optimization and Application Hosting by KLAS Research, the noted health IT data and insights enterprise.

TruBridge: Enhancing Community Health

As the health system evolves, small and community-based hospitals are increasingly challenged to keep their heads above water while adopting new technologies. TruBridge, formerly CPSI, solves these challenges by offering integrated EHR technology and revenue cycle management (RCM) solutions for community hospitals and clinics.

At HIMSS 2025, TruBridge highlighted how its solutions help health providers optimize claims processing, improve reimbursement cycles, and reduce claim denials. Additionally, TruBridge supports Social Determinants of Health (SDOH) data integration, enabling health providers to address barriers to care like food insecurity and transportation issues, particularly common in rural and underserved populations.

“It’s critical that all Americans have access to high-quality healthcare. Sparta Community Hospital and Artesia General Hospital lead by example through strategic SDOH data collection to identify and address community needs. Their presentation at HIMSS ‘25 revealed lessons learned and best practices for other leaders on the path to improved healthcare access and outcomes. These facilities demonstrate strong community commitment, and we are honored to work alongside them to help solve SDOH challenges and keep care local,” said Wes Cronkite, Chief Technology and Innovation Officer at TruBridge.

TruBridge solutions help hospitals remain operationally efficient while improving the quality of care for their communities. This ensures that hospitals remain viable and continue serving their populations effectively.

Unite Us: Integrating Social Care into Health

In an increasingly connected world, health providers recognize the critical importance of addressing social determinants of health (SDOH), such as housing, food insecurity, and transportation. Unite Us is integrating social care into health systems, creating cross-sector partnerships to ensure that patients receive holistic care that includes both medical and social support.

Through its platform, Unite Us connects health organizations with community-based organizations, creating a seamless referral process that allows providers to track and manage patient progress across medical and social services. Their closed-loop referral system ensures that patients receive the care they need, whether it be housing support or mental health services.

By addressing the root causes of health disparities, Unite Us enables health systems to provide more comprehensive care, improving health outcomes and reducing costs.

Vibe Health by eVideon: Transforming Patient Engagement

Vibe Health by eVideon demonstrates how smart hospital rooms equipped with interactive technology transform patients’ experiences of care. Their solutions, such as smart TVs, digital whiteboards, and bedside tablets, empower patients to take a more active role in their care while reducing the burden on clinicians.

“At HIMSS, we shared how Nebraska Medicine uses Vibe Health by eVideon’s smart hospital room solutions to enhance patient engagement and improve clinical efficiency. Technologies like interactive Smart TVs, digital whiteboards, and digital door signs help us streamline communication and improve the patient experience. Our discussion explored how technology drives meaningful improvements in both efficiency and outcomes. HIMSS provides a valuable space to connect with peers, exchange ideas, and explore innovative solutions that drive meaningful change across the industry,” share colleagues Ron Carson, Executive Director of Enterprise Applications, and Calli Sibilia, Program Manager – Innovation Design Unit at Nebraska Medicine.

Hospitals like Nebraska Medicine have integrated Vibe Health solutions to streamline communication and care coordination. Their interactive platforms give patients real-time access to care plans, educational materials, and appointment reminders, ensuring they are always informed. This digital-first approach enhances patient satisfaction and improves clinical efficiency by reducing time spent on manual updates.

Vibe Health creates a more efficient and effective health system by providing hospitals with cutting-edge tools to enhance the patient and floor-staff experience.

Bringing It All Together: A Holistic Approach to Health Information

The most promising aspect of the current health innovation landscape is that companies are beginning to address not just one aspect of the health journey but the entire continuum—from pre-hospital to post-care.

Companies like TruBridge are improving care for underserved populations by addressing the administrative burden and ensuring that small and rural hospitals can thrive. Vibe Health by eVideon transforms the patient experience by integrating interactive digital platforms into hospital rooms. At the same time, Unite Us ensures that social determinants of health are addressed at the patient’s first point of contact. Tegria is helping hospitals optimize their operations to ensure these innovations can be scaled effectively.

This approach helps break down the silos that have traditionally existed in health—where care delivery, operational management, and patient engagement are often managed separately. By unifying these elements, health innovators provide solutions that enhance patient outcomes, streamline operations, and improve the health experience.

A Future-Focused System

The health landscape is becoming ever more digital- and data-driven; expect an increasing number of companies that can create synergies across various technologies, pushing boundaries in areas like personalized medicine, predictive analytics, and patient-centered care. The focus will continue to shift toward providing patients with proper care at the right time—whether through real-time monitoring, telehealth consultations, or personalized digital health experiences.

The strength of the health innovation system today is rooted in its collaborative nature, where varied technologies come together to create an efficient, patient-centric, and resilient health environment. The companies speaking, presenting, and networking at HIMSS are improving the quality of care and patient outcomes and addressing clinical and social factors. As this ecosystem evolves, it paves the way for a connected, proactive, and equitable health system where innovation opens the path to positive change for patients and providers.

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Top IT firms are racing to attract customers for their wearable tech advancements because of this promising future of the wearable business. Tech giants like Samsung, Apple, and Microsoft have released new products in recent years. And digital wearable health devices are showing promise in managing chronic conditions, promoting preventative healthcare, and improving patient engagement, according to research conducted in 2024.

Improvements in sensor technology, AI integration, and data analysis are also enabling more personalized and accurate health monitoring. However, there are still issues with data privacy, regulatory frameworks, and device validation that need to be resolved before these devices can reach their full potential. But today wearable devices are adding a new layer of both protection and vulnerability to our health and our health records.

When it comes to diagnosing and monitoring individuals, wearables might be useful. Worldwide health systems, like the National Health Service in England, have acknowledged the potential of wearable technology to aid with health care, and this acknowledgment has earned them a spot in the common strategic Long Term Plan. Wearables, however, are not just for patients’ specialized medical gadgets. For customers who are concerned about their health, top IT companies have started investigating wearable health devices.

With its January 2019 launch, the “NHS Long Term Plan” lays out NHS England’s goals for healthcare over the next decade, including how the organization will spend NHS funds to enhance patient care and health outcomes throughout the country.

According to a large body of research, wearables can help people take charge of their health by facilitating self-diagnosis, behavior modification, and monitoring. If the technology is to be used widely, it requires more promotion and support from providers to encourage uptake; more short-term investment to upskill employees, particularly in data analysis; and overcoming barriers to use, especially by improving device accuracy, are all factors that will contribute to greater wearable adoption and engagement.

Participatory health informatics (PHI) means considering the role of technology in assisting individuals with self-management and decision-making by also improving health literacy and the physician-patient relationship so that individuals can become more involved in the aspects of their health and care.

Historically, research in the PHI field has predominantly been based on social media and internet-based applications, with patient empowerment having been identified as the most common theme in this body of research. However, wearables are just beginning to be considered as part of PHI given recent technological advancements. Therefore, similar research is now required to examine whether wearables can empower individuals in ways similar to those regarding domains such as self-management, decision-making, and the physician-patient relationship.

And the inclusion of patient empowerment must also be calculated and addressed in a world where an almost collegial relationship is formed between patient and physician. This will be a major change from our past relationships where patients were poorly received once they began researching their illnesses on the Internet and questioned their healthcare provider. “Oh, you’ve been using Dr. Google, I see,” was a common put-down. Respect is receiving new consideration and pointing out our lapses in possible healthcare education and sensitive training toward patients must be required. Can challenging patients be acceptable, as formerly was the case?

Remote patient monitoring using wearables is becoming more important in light of the growing provider shortages that exacerbate geographically based disparities.

Where Do We Need Wearable?

One area of intense interest is related to patients with potential cardiac issues and here we note that much is yet to be accomplished for these patients. Only eight studies of the 31,12 papers retrieved from a systematic search were randomized controlled trials. The research was mostly focused on consumer-grade wearables that were modified to monitor heart failure (HF). The majority of these studies were conducted in the feasibility testing phase. Out of all the wearables that were mentioned, only two were approved by the FDA for HF RM (remote monitoring). A major obstacle to wearables’ incorporation into HF therapy is the lack of convincing evidence regarding their actual influence on HF management. This must be a concerning issue.

High expenses, low functional capacity and quality of life, and high rates of morbidity and mortality marked the complex and sometimes fatal syndrome known as heart failure. High expenses, low functional capacity and quality of life, and high rates of morbidity and mortality mark the complex and sometimes fatal syndrome known as heart failure (HF). Over 64 million individuals across the globe are impacted by HF and we do not truly know the total number because not everyone with potential HF has been identified. As a result, reducing its monetary and social impact has risen to the status of a top public health concern on an international scale. Therefore, this should be one of the aims of wearables — helping to reduce mortality.

But digitals have a whole host of potential uses currently that include fall detection, glucose monitoring, activity monitoring (steps tracking), sleep quality, blood pressure monitoring, and potentially high-risk pregnancies.

Unaddressed Issues of Wearables

Concerns about privacy and data sharing are only two of the many hazards and difficulties linked to wearable technology. The literature has mainly addressed technological or ethical concerns, treating them as distinct domains of study; nevertheless, wearables’ potential to enhance biomedical knowledge acquisition, development, and application has received scant attention.

Three areas that have been inadequately studied include screening, detection, and prediction. Screening involves searching through datasets obtained by monitoring for particular diseases and the people linked to them. Passive sensors that monitor things like motion, steps, light, pressure, sound, etc. are typically the basis of wearables used for this purpose.

One application of wearable technology is screening for sleep apnea by tracking the wearer’s heart rate and breathing patterns while they sleep. Detection is a process that is closely connected to screening. It is common practice to employ wearables to detect conditions and notify individual users when monitoring particular conditions in populations. Detection involves looking for patterns and features in the data gathered from wearable monitoring devices that could be indicative of certain medical disorders. Once we have this data, the possibility of greater detection of issues may emerge.

It is still very difficult to predict clinical outcomes following hospital discharge. There has been little success with strategies aimed at predicting and preventing death after discharge, readmissions, and trips to the emergency department (ED). Early interventions on modifiable risk variables might minimize morbidity, death, readmissions, and emergency department visits if predictive models could be improved. Wearable technology can monitor activity levels, sleep patterns, and tachy- or bradyarrhythmias, some of the modifiable risk factors for these clinical outcomes. Wearable digital devices might make significant differences in the ability to predict future health issues.

It has been shown that using more aspects of complex data from wearable technology would most likely enhance prediction models. One study that made use of 89 Fitbit data characteristics had an 88.3 percent success rate in predicting hospital readmission. Compared to other models, theirs performed far better in predicting readmission using typical retrospective clinical data.

Are digital, non-invasive wearables a viable and important contribution to healthcare now and in the future? Unquestionably, they are providing information as a continuous monitoring system in real-time and can prevent fatalities. The cost of the devices, availability, and insurance reimbursement will undoubtedly factor into how many patients can avail themselves of this technology. Lower-income patients and those in health deserts will be less likely to have access to them.

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While we should welcome ready access to health data, viewing it without the interpretation of a seasoned clinical expert can fuel fear. This is especially true when worrisome data prompts you to reflexively rush off to “Dr. Google” – a source that knows neither empathy nor accuracy.

On behalf of myself, my family, and the patients whom I know, I’d urge those engaged in managing and improving health tech to recognize what now essentially amounts to a gap in care. You must integrate responsible, clear, and reassuring interpretative information into your apps and platforms. For patients, the reason for the health system’s very existence is much more than a “nice-to-have” feature; supplying it is an ethical imperative.

Patients should not be left to parse complex lab data alone, nor should they be making decisions based on fragmented information, especially when a single abnormal result might suggest a more serious situation than exists. While AI-generated engines process and summarize vast amounts of information in a blink, they lack the clinical judgment and humanity of a physician who knows a patient’s medical history. 

Further, AI-based models cannot currently account for every factor influencing personal health metrics, such as lifestyle, age, genetic predispositions, or recent health events. Even the language used by AI can inadvertently amplify fear; a model might describe results as “abnormal” or “high-risk” when the variation is clinically insignificant. And because AI still lacks a provider’s empathy and human touch, patients may rightly feel uneasy receiving sensitive information without the guidance of a health professional.

The Prostate Cancer Anxiety Trap: Low PSA and Free PSA

I recently underwent a routine prostate cancer screening, where the lab results appeared to signal a high risk. But without knowledgeable interpretation, the data I received was incomplete and could easily have been misleading. Consider my PSA (Prostate-Specific Antigen) tests: while a low PSA level, around 1 ng/mL, is well within the safety margin, a low percentage of Free PSA—such as 10%—could indicate a higher risk of prostate cancer, even when the PSA level itself does not suggest immediate concern. Interpreting these results on my own, the low Free PSA percentage triggered no small amount of anxiety about prostate cancer.

Conversely, a low PSA level combined with a low Free PSA percentage does not paint a clear picture; further medical consultation was required to interpret risk accurately. Age, overall health, and family history are crucial in clinical decision-making, but these factors are absent from raw diagnostic data. I was lucky to have the immediate benefit of my urologist’s expertise; without their judgment and insight, I might have jumped to conclusions, undergone unnecessary stress, or requested unneeded tests out of fear.

And it’s not just prostate exams. Thyroid function tests, heart-health markers, and diabetes screening metrics are three additional areas rife with potential confusion for patients reviewing their raw lab results. Elevated TSH readings, high LDL or CRP levels, or a single HbA1c result do not tell the whole story. These values are not necessarily predictive independently; they’re components of a broader clinical picture. Consumers need to be supported in reviewing that clinical picture, and AI needs to be part of providing that help.

Moving Toward Empowered, Informed Health Access

Despite the overall need to incorporate supports for interpreting health data, some health diagnostic platforms have begun to address consumers’ need to know by adding helpful explanatory notes to lab results or providing easy-access telehealth consults for patients seeking immediate explanations. A patient receiving an abnormal test result might be able to speak with a health professional or receive brief, plain-language insights about what the numbers indicate in general terms. These options can serve as bridges, guiding patients away from alarmist conclusions and toward actionable steps. At the very least, they provide patients with prompts for the questions to ask their health professional during a face-to-face meeting.

For those building and maintaining portal apps and platforms, acknowledging that many patients lack extensive health literacy and may be unfamiliar with basic medical terminology is crucial. Developers of health platforms should consider embedding visual aids, simplified summaries, or links to reputable health information sources to provide a foundation for stronger understanding before patients seek further information.

AI and Large Language Models: Help and Hazard

A recent JAMA study surprisingly suggests patients often prefer ChatGPT tools to physician conversations. While physicians understandably doubted the study’s conclusions, its data and the public conversation it sparked revealed that technology available in real-time to answer patients’ pressing clinical and emotional needs was sought and welcomed.

Large language models (LLMs) can provide general context, describing what test results typically mean and suggesting possible follow-up actions, like consulting a doctor if certain thresholds are exceeded. Technology can offer helpful insights, especially for people with conditions and concerns like cancer and other frightening health challenges requiring prompt access to professional input.  

LLM Cancer Mentor Apps such as Dave AI are revolutionary tools for cancer patients, caregivers, and physicians. It’s like having a WAZE for navigating the complex cancer diagnostic and care journey. Patients no longer have to wait for their next appointment to get answers to their questions.  Health tech innovators realize that diagnoses leave patients anxious between doctor visits, and the best solution may often be well-trained LLMs.  [See LLM Cancer Mentor “Dave AI” Offers WAZE-like 24/7 Personalized Support, Making it a Game-Changer in Patient Care.]

The Cost of Health Data Without Context

The rapid consumerization of health data has democratized healthcare in powerful ways, but its pace has opened gaps in care and introduced risks. The emotional toll can be high when consumers receive real-time results without contextual guidance. Data that might otherwise encourage patients to engage actively in their wellness may instead provoke anxiety, confusion, or even mistrust in their own body’s signals.

“In clinical trials and healthcare, acknowledging and addressing the psychological symptoms brought on by the diagnosis, prognosis, and treatment of many conditions is vital,” writes Emily Epstein, LMSW, a Weill Cornell staff members who advocate for the inclusion of mental health support research and medical settings. “When a provider overlooks the anxiety that accompanies the stress of managing physical symptoms, a disservice is done to the mental well-being of the patients and participants, which can have adverse effects on the trial itself,” she adds.

A Call for Physician-Guided Digital Health Experiences

Access to lab results is part of a significant and positive cultural shift in health, where the focus has moved toward patient empowerment and shared decision-making. However, for true empowerment, patients need more than raw data; they need empathetic guidance in understanding their data’s meaning in the context of their unique health profile. To address this, health systems and app developers should incorporate sophisticated LLM resources that help educate users about the strengths and limitations of raw lab data and how to navigate the journey better ahead.

Empowered patients are educated patients capable of better understanding their health journeys in partnership with their health providers. As we advance, let’s strive for a digital health environment where patients can access their results clearly and confidently and know that communication is always part of the care.

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The Marfan Foundation is the world’s most extensive patient and professional community addressing the needs of people living with genetic aortic and vascular conditions, serving one million people with educational materials and reaching 3.2 million people in the digital space annually.

The Marfan Foundation will play a crucial role in informing individuals living with Marfan, Loeys-Dietz, Vascular Ehlers-Danlos syndromes, and related conditions about the resources and community dialogue offered by StuffThatWorks. This collaboration will enable StuffThatWorks to develop a comprehensive strategy for engaging non-profits with the goal of ultimately benefiting the global patient community.

“We’re honored to have been selected as the first non-profit to align with StuffThatWorks to elevate community members’ voices, foster additional personal empowerment, and advance access to potential learnings that may be derived through global self-reporting,” said Michael L. Weamer, CEO of The Marfan Foundation.

StuffThatWorks’ operational value is that patients’ crowdsourcing information can highlight issues that need to be addressed from the patient perspective as well as spotlight various treatment options, side effects, and obstacles to care: “Understanding how patients experience various treatments is one of them,” according to their website. “The Marfan Foundation has a steadfast commitment to scientific rigor and fostering a strong global community. Operationally, the Foundation leverages world-class experts to focus on research and best practices in patient engagement and empowerment,” says Yael Elish, CEO of StuffThatWorks. “The Foundation’s investment in basic and applied research and translating these understandings into patient support and education reinforces that real-world data is valued in applications with the potential to advance treatments and quality of life.”

“We’re enthused to share what we know about specific non-profit needs and goals while we learn more about the possibilities StuffThatWorks’ patient-centric knowledge-base will offer,” said Weamer.

About 1 in 5000 people have Marfan syndrome, including men and women of all races and ethnic groups. Roughly 3 out of 4 people with Marfan syndrome inherit it. There is a 50 percent chance that a person with Marfan syndrome will pass along the condition each time they have a child. Because connective tissue is impacted and found throughout the body, Marfan syndrome can affect many body parts. Features of the condition are most often found in the heart, blood vessels, bones, joints, and eyes. Some Marfan features – for example, aortic enlargement (expansion of the main blood vessel that carries blood away from the heart to the rest of the body) – can be life-threatening. People living with Marfan syndrome and related conditions have a 250 times greater risk of aortic dissection than the general public. The lungs, skin, and nervous system may also be affected.

About StuffThatWorks

Created by Waze founding team members, StuffThatWorks uses crowdsourcing and AI to empower patients to transform their experiences into organized, large-scale structured Real-World Data needed for research. StuffThatWorks is the home to three million members across 1250 condition communities that have shared 1.3B data points. Now the largest organized Patient Level Real World Data platform, StuffThatWorks is differentiated by its expansive data collection, structuring, and organization of accessible information. The unique proprietary data set and unique AI and powerful Chat GPT-like capabilities enable the generation of insights for research, market access, and drug development.

Crowdsourcing Treatments that Work | Community Research | StuffThatWorks

About the Marfan Foundation

The Marfan Foundation is a global nonprofit organization that empowers people with genetic aortic and vascular conditions to foster optimal quality of life and longevity while building community. We save lives through research and education, enabling healthcare providers to offer the best-quality treatment and helping to foster mental and physical wellbeing. We serve communities impacted by Marfan syndrome, Loeys-Dietz syndrome, Vascular Ehlers-Danlos Syndrome, and related conditions. To learn more, visit marfan.org or meet us on social media:

Facebook | Instagram | LinkedIn | X (Twitter) | Threads | YouTube | TikTok

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With an impressive $12.4 billion invested across 719 deals, this period defied expectations of a prolonged funding winter and showcased remarkable growth. These deals span a wide range of digital health subsectors, from AI and LLMs to health information management and connected medical diagnostics. The industry saw a significant surge in momentum in Q2 2024, outperforming Q1 with $7.1 billion in funding compared to $5.3 billion.

Key areas such as AI, LLMs, health information management, connected medical diagnostics, and research solutions (i.e., TechBio) have emerged as focal points for VC and corporate incubators, accelerators, and equity funds, so critical to providing the financial and strategic support necessary to jump-start early-stage companies. A steady stream of M&A activity and strategic partnerships indicate ongoing global digital health market consolidation.

Galen Growth has been tracking every detail and deal and analyzing the digital health sector’s ups and downs, and it has now returned to the forefront of the C-Suite. To arrive at its analyses, Galen Growth tracks some 500 million data points across 30,000 sector players. Medika Life was able to preview some of the data to provide these highlights. Our assumption all along is that the spike in COVID-era investment was pure enthusiasm absent due diligence and that the category would rebound. This report confirms that hypothesis.

Medika Life readers can access the H1 2024 report here:

https://www.healthtechalpha.com/research/h1-2024-digital-health-global-key-trends-report

H1 2024 Growth

The $ 12.4 billion investment in the first half (H1) of 2024 is a significant milestone, marking a 5% increase compared to last year’s H1 2023. This robust performance challenges the narrative of a funding winter, showcasing sustained investor confidence. Q2 outpaced Q1 significantly, with a 34% increase in funding, indicating growing momentum.

“Anyone who thought digital health growth is stagnant was proven wrong by this recent finding data.  Investors find the value in these innovations – the challenge that remains is about the culture of the health sector – the role of the health care community in embracing these technologies,” reflects WebMD Chief Medical Officer John Whyte, MD, MPH, who has been a national voice on the potential of smart medical devices to improve people’s care.

More than 1,600 investors participated in H1 2024, with 50 making more than three investments, up from 47 in the previous year. This suggests a renewed confidence and a growing core of committed digital health investors. Key indicators such as the high number of mega deals and the significant proportion of funding directed towards AI-enabled companies underscore this trend.

Key Trends and Insights

Generative AI and AI- and LLM-enabled companies continue to gain investor attention, particularly in health information management. Despite comprising only 40% of the venture ecosystem, AI-enabled companies received 56% of the venture capital in the sector. This trend highlights the critical role of AI in driving innovation and attracting significant investment in healthcare.

“I believe that AI and large language models represent a new and unique level of revitalization for digital health.  The utility and functionality of many digital health devices can be transformed through artificial intelligence and large language models (LLMs). It’s this fundamental transformation that breathes life into what was a lackluster and ‘gadget-based’ initiative,” offers John Nosta, a global innovation thought leader who speaks to the “Age of Cognition” and how it impacts the future of medical care and technology.

The US and North American countries led digital health funding, while Europe remained the second-most funded region. Future reports will provide deeper insights into these trends with a detailed regional breakdown and analysis.

Mega-Deal Investments

Mega deals were crucial in the Digital health funding landscape during H1 2024. The period saw 26 mega deals (deal value ≥ $100 million) totaling $4.9 billion, accounting for 39% of the overall funding. This represents a 9% increase from H1 2023, which recorded 24 mega deals amounting to $4.5 billion. The average mega deal size reached its highest since 2019, at $191 million.

“The significant increase in mega deals and the robust M&A activity in H1 2024 highlight a maturing ecosystem rapidly consolidating and strategically repositioning itself,” said Galen Growth Founder and CEO Julien de Salsberry to Medika Life. “These insights are crucial for stakeholders to understand the evolving dynamics of the market and to make informed decisions that will drive future growth and innovation in the Digital health space.”

Notable Mega Deals:

• Xaira Therapeutics – $1,000 million (ARCH Venture Partners, Foresite Labs)
• Formation Bio – $375 million (Andreessen Horowitz)
• Freenome – $254 million (Roche)
• PharmEasy – $216 million (MEMG)
• Blackrock Neurotech – $214.3 million (Tether)

The geographical distribution of these mega deals highlights continued US dominance in attracting substantial investments. Of the 26 mega deals, 22 were secured by U.S.-founded companies, underscoring this nation’s engaged digital health ecosystem.

US Digital Health Funding by Investment Stage

The distribution of funding across various stages suggests a vibrant investment cadence supporting companies at different growth phases:

• Early-stage deals: 132 deals (32% of all deals)
• Series A: 88 deals
• Series B: 65 deals
• Series C: 42 deals
• Series D & Beyond: 35 deals
• Other Stages: 43 deals across various other stages.

Acquisitions and Sell-Offs

Mergers and acquisitions (M&A) activity in the Digital health sector remained robust in H1 2024, with 96 deals completed, a slight increase of 2% from H1 2023. This steady pace of M&A transactions demonstrates the industry’s ongoing consolidation and strategic repositioning.

• Noteworthy Transactions:
• Altaris Capital Partners acquired and delisted Sharecare for $518 million.
• Roche acquired the LumiraDx point-of-care technology platform for $295 million.
• Click Therapeutics acquired assets from the bankrupt Better Therapeutics.
• LabCorp acquired some assets from Invitae, a genetic testing company.

The elevated activity in M&As and asset acquisitions reflects the dynamic nature of the Digital health sector, with companies adapting to market conditions and consolidating resources.

Partnerships and Collaborations

The Digital health sector witnessed an almost unchanged collaborative environment during the first half of 2024, with 1,730 partnerships recorded, a slight 2% decrease from H1 2023. Healthcare providers emerged as the primary drivers of partnerships, forging 306 collaborations. This underscores the growing integration of digital solutions in traditional healthcare settings.

Newsworthy Collaborations:

• Nvidia – Nine partnerships focusing on health AI applications.
• Medical Diagnostics and Health Management Solutions – Key areas for venture-to-venture partnerships, indicating a trend towards leveraging complementary technologies and expertise.

Regional Funding Snapshots

United States: The U.S. maintained its dominant position, attracting $9.5 billion across 405 deals, up 14% year-over-year. The distribution of funding across various stages reveals a revived and focused ecosystem supporting companies along the growth cycle.

Europe: Despite a 10% decrease in funding to $1.52 billion across 158 deals, Europe remains the second most heavily funded region. The funding landscape showcases diverse investment stages, indicating a healthy and maturing ecosystem.

Asia Pacific: Asia Pacific attracted $1.17 billion across 118 deals, down 7% from H1 2023. Despite the decreased funding, the region remains strong in diagnostic technologies and patient-centric digital solutions.

Overview

The first half of 2024 has demonstrated the resilience and evolution of the digital health sector. With substantial investments and strategic collaborations driving innovation and growth, the $12.4 billion funding across 719 deals underscores renewed investor enthusiasm. The sector’s solid M&A landscape and increasing mega-deal activity convey a maturing ecosystem poised for continued integration into the mainstream health ecosystem.

As digital health ventures in the US, Europe, and Asia Pacific push the boundaries of health innovation, the groundwork laid during H1 2024 suggests a transformative future for the industry. Data from H1 2024 means that the digital health venture C-suite and investors can breathe a sigh of relief as valuations stabilize and deal flow continues.

This outlook calls for careful and informed decisions driven by proof points and facts, steering away from the speculative fervor of the COVID era.   The reflections of Galen Growth’s Julien de Salsberry reinforce that digital health – an expansive innovation field – that warrants payers, providers, and product innovators full attention:

“The Digital health sector’s resilience and sustained growth, as evidenced by the $12.4 billion investment in H1 2024, is a testament to the unwavering confidence of investors in the transformative potential of digital health technologies. This data underscores innovation’s critical role in enhancing healthcare delivery and outcomes globally.”

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