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SCOPE Summit is Expected to be a Major Platform for News in Clinical Trial Innovation

Medika Life — Mon, 02 Feb 2026 18:05:30 +0000

As the SCOPE Summit unfolds over the coming days, a steady stream of announcements is shaping how clinical research teams think about feasibility, enrollment, and execution. These are not abstract discussions. They are practical responses to long-standing friction points in trial design that slow studies down and distance protocols from the realities of patient access.

One of the early signals to watch comes from PhaseV, which used the opening of the meeting to introduce its new AI-powered Enrollment Lab. The launch reflects a broader shift visible across SCOPE this year. Sponsors and CROs are moving upstream, earlier in the development lifecycle, to replace assumption-driven planning with evidence-grounded decision-making.

Neta Shanwetter Levit, Director of Clinical & Data Operations at PhaseV demonstrates for Medika Life the company’s new AI-powered Enrollment Lab that helps life science companies and CROs optimize their clinical trial design and recruitment protocols.

Enrollment remains one of the most persistent causes of trial delay. Protocols are often finalized before teams fully understand how inclusion and exclusion criteria interact with real world patient availability or with competitive trial pressure. PhaseV’s Enrollment Lab is designed to address that gap by allowing study teams to quantify true enrollment potential before protocol lock, not after sites have already struggled.

Built as a high impact extension of the PhaseV ClinOps platform, the Enrollment Lab leverages real world EHR data to model patient eligibility alongside patient level competition. Instead of relying on theoretical projections or late stage site surveys, sponsors can explore how specific protocol trade offs affect enrollment volume in real time. Small design decisions become visible for what they are, either barriers to access or opportunities to expand reach.

Raviv Pryluk, PhD, CEO and co-founder of PhaseV, described the intent as a shift from planning on paper to planning in clinical reality. By uncovering constraints early, teams can stress test their designs and ensure that studies are grounded in a verified patient population before site identification begins. That change in timing matters. It moves feasibility from a reactive step to a strategic one.

From a technical perspective, the Enrollment Lab extends PhaseV’s population first approach. Elad Berkman, CTO and co founder, emphasized the ability to translate protocol choices and competitive pressure into a clear view of real patient access. This precision guided modeling allows teams to evaluate alternative inclusion criteria, identify lightly contested patient segments, and surface geographic regions that may be overlooked by traditional planning methods.

Positioned early in the workflow, the Enrollment Lab establishes what is realistically achievable before resources are committed downstream. Once realistic enrollment potential is defined, PhaseV’s site identification tools can then prioritize investigators based on their ability to deliver against that plan rather than aspirational targets. The result is tighter alignment between protocol design, site selection, and execution.

As conversations continue throughout SCOPE, this launch is likely to gain strong traction among sponsors focused on shortening timelines without compromising scientific rigor. The message aligns with what many leaders here are emphasizing. Faster trials do not come from pushing sites harder. They come from designing studies that reflect how patients actually exist within health systems.

PhaseV is demonstrating the Enrollment Lab throughout the Summit, offering attendees a closer look at how real world data can be applied before the first site is ever activated. For an industry increasingly focused on predictability and patient centricity, this announcement sets an early tone for the news still to come out of SCOPE in the days ahead.

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Medika’s Top 20 AI Voices to Watch. Follow These Experts as They Demystify AI and Its Future

Gil Bashe, Medika Life Editor — Thu, 17 Aug 2023 14:00:00 +0000

Is “AI” threatening and misunderstood? Absolutely! The term alone – “artificial intelligence” – is a worrisome misrepresentation of an idea that people will become secondary to machines that are able to process vast amounts of data accurately and in a blink. What happens if we realize that artificial intelligence will not transform people’s health or the system – rather it’s the human desire to question, collaborate and generate shared – composite – wisdom that is core to innovation and improved care. We can call upon (human) experience and intelligence to “augment” what we know and apply!

“Culture crushes innovation.” Here, we must not point the finger of blame for the dysfunctional health ecosystem at physicians, nurses and allied health professionals on the hospital floor. Healers want to heal as much as those who need their help seek answers and solutions. The shared problem is not how to utilize this “hot” evolving technology but are we prepared to make the investment in time and training! Time to input (usable) data into EMRs and learning technologies that can sort and “scrape” through and highlight information to accelerate clinical decision-making.

Using AI to Reject Patient Claims and Care

One part of the challenge is understanding how to cognitively maximize and explore vast amounts of information is another. The other is to come to agreement in how AI can and cannot be used ethically and responsibility. As we saw recently, a nascent AI system must not be used by major health insurance companies to deny patients coverage for medicines and procedures.

While the “system” is resistant to significant change and science has become politicized and misunderstood, we must rally to the dual-goal of saving lives and reducing costs. Can information technologies and “intelligent health systems” do what people in power have not achieved? Are we able to educate consumers about being partners in data security? Can information bring people – professionals and consumers – together and transform this country’s post-WWII build health system? Yes!

Cardiologist Eric Topol, MD, reflects on the tension between patient care and AI in a Time Magazine interview about his bestselling book Deep Medicine:

“AI will always need human backup. A machine could handle certain things autonomously–diagnosing a skin rash, for example. Doctors shouldn’t be dealing with things that machines will do better than them. But serious conditions, like getting a cancer diagnosis, are what doctors should be working on. I think once patients understand that there are things they don’t need doctors to do, they would love it–once they get used to it.”

Many health professionals (and patients) are understandably wary! In one Medscape interview a physician shared this experience:

“We’ve played around with it. It was very early on in AI and we noticed it gave us incorrect information with regards to clinical guidance,” said Monalisa Tailor, MD, an internal medicine physician at Norton Health Care in Louisville, Kentucky. “We decided not to pursue it further,” she said.

Don’t close the door on an evolving technology! Care professionals work within a system that hinges on economics – how the institution is paid and how it must cover costs. What is to be done if the system cannot support the health professional’s efforts or make the best use of their training, abilities, energy and dedication? What will happen as more and more health professionals ghost the provider system that they feel no longer addresses their emotional and economic needs? These are questions that must be addressed sooner than we think! AI is inescapable. It’s an evolutionary process that will improve – improve health professionals and care output – over time.

Perhaps the biggest question is whether we can wait to resolve all these questions while health costs soar and people risk dying from treatable illnesses. We should already know that answer! If you’re living in the question, that’s great. One way to resolve your angst and uncertainty is to follow people – some pioneers and others pundits – who are also asking those questions, doing the necessary research and exploring sensible possibilities.

20 AI/ChatGPT Champions to Follow

It was a challenge to select only 20 influentials for this listing. There countless people writing, speaking and commenting about AI and ChatGPT. It’s a hot topic and “trendy.” This list is not about “trends” and it isn’t an exclusive roster of people. However, these people are key voices of influence at the frontlines of AI and ChatGPT and are boldly considering how the health sector will need to support and adapt to these technologies ethically. It is important to follow their social posts, speeches and writings. They are theorists, physicians and teachers. What they say and where they say it can inform your opinion and direction. Follow them and get smarter faster!

Acknowledgements

As always, thanks to my friend and Medika Life Founding Editor-in-Chief Dr. Robert Turner, and Cullen Burnell for their encouragement and suggestions, and to Carolyn Neugarten, Medika Life Editorial Assistant for her efforts in making this feature possible, and Tyler Mayers, whose expertise sharing content across social platforms makes important information widely accessible. Special appreciation to colleagues from numerous professional circles who shared their thoughts and contributed recommendations. Special acknowledgement to people who appeared on previous Medika Life Lists and continue on the path to improve the wellbeing of people and planet. These exemplary people are dedicated to improving people’s health and know their efforts will make a difference.

AI and ChatGPT are frightening to some and mysteries to others. Humanity must decide how to best deploy these evolving and “thinking” technologies. What to do next is a mindful choice in the hands of people, and must be for the sake of people.

Gil Bashe, Editor–in-Chief, Medika.Life

AI and ChatGPT Global Voices of Influence

Here is our Medika Life roster of 20 people to follow in alphabetical order; all-important voices – to track and learn from in the coming years We have included wherever available links to the social media platforms and bios.

Follow these Voices and Leaders of Influence – They are writing the future of how AI and ChatGPT will be essential to health care delivery.

Ran Balicer, MD

Ran D. Balicer, MD

Dr. Ran D. Balicer, a public health physician, executive and researcher, serves as Chief Innovation Officer & Deputy-DG at Clalit – Israel’s largest health insurance organization, and is Founding Director of the Clalit Research Institute.

Regina Barzilay, MD

Dr. Regina Barzilay is a School of Engineering Distinguished Professor for AI and Health in the Department of Electrical Engineering and Computer Science, and a member of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology (MIT).

Kirk Borne, PhD

Dr. Kirk Borne is a Data Scientist, providing thought leadership, global speaking, content creation, mentoring, training, and consulting activities in data science, machine learning, and AI across multiple disciplines.

Atul Butte, MD

Atul Butte, MD, PhD

Atul Butte, MD, PhD is the Priscilla Chan and Mark Zuckerberg Distinguished Professor and inaugural Director of the Bakar Computational Health Sciences Institute (bchsi.ucsf.edu) at the University of California, San Francisco (UCSF).

Lex Fridman, PhD

Lex Fridman, PhD, is a Russian-American computer scientist, podcaster, and artificial intelligence researcher. He is a research scientist at the Massachusetts Institute of Technology (MIT), and hosts the popular Lex Fridman Podcast, a podcast and YouTube series.

Mo Gawdat

Mo Gawdat, MBA

Mohammad “Mo” Gawdat is an Egyptian entrepreneur and writer. He is the former chief business officer for Google X and author of the bestselling business books Solve for Happy and Scary Smart – The Future of Artificial Intelligence and How You Can Save Our World.

John Halamka, MD, MS

John D. Halamka, M.D., M.S., president of the Mayo Clinic Platform, was trained in emergency medicine and medical informatics, Dr. Halamka has been developing and implementing healthcare information strategy and policy for more than 25 years.

Eric Horvitz, MD

Microsoft’s Chief Scientific Officer and an early AI champion for health and medicine going back many years, Dr. Eric Horvitz is among the founders and funders of Stanford’s 100-year longitudinal study of the impact of AI on Society.

Daniel Kraft, MD

Daniel Kraft, MD, is a Stanford and Harvard-trained physician-scientist, inventor, entrepreneur, and innovator and is serving as the Chair of the XPRIZE Pandemic & Health Alliance. With more than 25 years of experience in clinical practice, biomedical research and health innovation.

Tom Lawry

A leading AI transformation advisor to health and medical leaders around the world. Lawry is the best-selling author of Hacking Healthcare – How AI and the Intelligent Health Revolution Will Reboot an Ailing System, former Microsoft National Director of AI and now the managing director of Second Century Tech LLC, and partner in Aspire Health Innovations.

Fei-Fei Li, PhD

Dr. Fei-Fei Li is the inaugural Sequoia Professor in the Computer Science Department at Stanford University, and Co-Director of Stanford’s Human-Centered AI Institute. She served as the Director of Stanford’s AI Lab from 2013 to 2018.

Berci Mesko, PhD

Berci Mesko, MD, PhD

Bertalan Meskó, PhD is known as “The Medical Futurist” and the Director of The Medical Futurist Institute analyzing how science fiction technologies can become a reality in medicine and healthcare. He holds a PhD in genomics and is an Amazon Top 100 author.

Mira Murati

Mira Murati is the Chief Technology Officer at OpenAI, and one of the most influential innovators in technology and AI. She oversees efforts to commercialize OpenAI products and improve them through human feedback. She serves on the leadership teams that developed the OpenAI ChatGPT and Dall-E systems.

Andrew Ng, PhD

Dr. Andrew Ng is a globally recognized leader in AI. He is Founder of DeepLearning.AI, Founder & CEO of Landing AI, General Partner at AI Fund, Chairman and Co-Founder of Coursera and an Adjunct Professor at Stanford University’s Computer Science Department.

John Nosta

John Nosta is a cutting-edge thinker in the world of technology, science, medicine and innovation. He is a globally recognized among the foremost voices constantly pushing the envelope on how AI and ChatGPT will transform health settings and work flow. Nosta is non-stop power thinker (and contrarian) and has more than 500 articles published on innovation topics.

Tam C. Nyugen

Tam C. Nguyen

Associate Professor Tam Nguyen has more than 20 years of working in clinical trials, health and medical research and research management sector including tertiary teaching hospitals, medical research institutes and universities across Australia. He is the co-editor of the recent book: AI in Medicine (Springer).

Ziad Obermeyer, MD

Ziad Obermeyer, MD, is the Blue Cross of California Distinguished Associate Professor of Health Policy and Management. He is a physician and researcher who works at the intersection of machine learning and health.

Brian Roemmele

Brian Roemmele is a scientist, researcher, analyst, connector, thinker and doer. Brian became an early adopter of AI and neural networks and pioneered Prompt Engineering technologies and techniques to understand and extract information from AI platforms.

Mihaela van der Schaar, MD

Mihaela van der Schaar, MD, is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Fellow at The Alan Turing Institute in London. In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM).

Eric Topol, MD

Eric Topol, MD, is the founder and director of the Scripps Research Translational Institute; Executive Vice President, Scripps Research; Professor, Molecular Medicine, Scripps Research; and the Gary and Mary West Endowed Chair of Innovative Medicine, Scripps Research. In many ways, Dr. Topol was he kindled that started the AI transformation with his more than 1,300 publications and bestselling books.

Dr, Topol appears last – alphabetically – on this Top 20 Voices in AI and ChatGPT to follow. His ideas and prodding the system, perhaps place him first!

Spot a broken link, typo, forgotten degree or name misspelled?

L-R – Tom Lawry, Daniel Kraft and Atul Butte at Stanford unite to share ideas and champion AI and ChatGPT – innovation is always a “team sport.”

Oops! Apologies! While care has been taken in compiling this list and adding social media links, things go wrong or a name is misspelled or a link broken. Please do get in touch to address typos, amendments, or omissions by reaching gil.bashe@medika.life.

Thanks in advance and be healthy!

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Digital Health Adoption: Maybe It’s Not the Pace, It’s the Idea

John Nosta — Wed, 07 Jun 2023 20:17:12 +0000

Healthcare has witnessed substantial transformations throughout history, from medical breakthroughs to technological advancements. Rapid adoption isn’t as uncommon as one might perceive in this sector, especially when an innovation fills a significant void or simplifies a complicated process. Pulse oximetry, for instance, quickly became an indispensable tool in healthcare due to its capacity to provide real-time, non-invasive measurement of oxygen saturation levels, thereby revolutionizing patient monitoring. However, the integration of digital health into the system, brimming with potential to upend traditional practices, has experienced a somewhat slower adoption pace. It begs the question: is the core issue rooted in the pace of adoption or the concept of digital health itself?

The Adoption Disappointment

Digital health includes a plethora of technologies like electronic health records (EHRs), telemedicine, wearable devices, artificial intelligence (AI), and personalized medicine. Each one of these has the potential to revolutionize healthcare, making services more efficient, reducing costs, and improving health outcomes.

Yet, for all its promise, the adoption of these digital health technologies has been slower than anticipated. This brings us to ponder if the real issue is not with the speed of acceptance, but rather with the idea itself.

Factors Influencing Adoption

To better understand the reasons behind this paradox, it’s necessary to delve into the key elements influencing the adoption of new technologies in healthcare: clinical need, integration into existing workflows, cost, and clinical validation.

Clinical Need: A new technology, regardless of its advanced nature, will only garner popularity if it addresses a compelling clinical need. Pulse oximetry, for example, was quickly adopted because it filled an urgent gap in the field of patient monitoring that was much less invasive than the old-school arterial puncture.

Digital health technologies should similarly prove that they can address real and pressing clinical challenges. Technologies that just duplicate current procedures without offering significant advantages may find their adoption lagging, whereas those offering substantial improvements are likely to gain traction faster.

Integration into Workflow: Healthcare professionals often work under high-stress conditions, with little room for additional burdens. Technologies that seamlessly integrate into existing workflows, such as pulse oximetry, gain rapid acceptance since they provide critical information without increasing the workload.Digital health technologies should follow suit, integrating smoothly into existing healthcare processes. This requires not only technological compatibility, but also an understanding of the working habits, communication patterns, and patient management approaches of healthcare professionals.

Cost: Healthcare is already grappling with escalating costs. New technologies, if expensive, can add to this burden. Innovations that provide cost savings are therefore more likely to be embraced.

Although many digital health technologies promise long-term cost savings, the upfront investment can be substantial. Therefore, demonstrating the cost-effectiveness of digital health remains an essential part of the equation.

Clinical Validation: Last but not least, robust clinical validation is essential for any new healthcare technology. This is typically achieved through rigorous, peer-reviewed studies that demonstrate the technology’s safety and effectiveness.

The track record of digital health technologies in terms of clinical validation is somewhat mixed. While some technologies have a strong evidence base, others have been brought to market with minimal validation. This lack of evidence can hinder adoption, as healthcare professionals are naturally wary of unproven technologies.

User Experience: Undoubtedly, the user experience is a pivotal factor in the acceptance of digital health technologies. Ultimately, the value of digital health technologies will be realized when they seamlessly blend into the healthcare journey, improving the user experience, and fostering a more patient-centric approach to care.

So, Is It Really the Idea?

Considering the multitude of challenges facing digital health, it may seem tempting to conclude that the problem lies with the idea itself. However, this oversimplifies the issue.

The concept of digital health is robust and promising. It has the potential to effect seismic shifts in healthcare, leading to better patient care at lower costs. However, like any disruptive innovation, it faces hurdles. The key to faster adoption lies not in discarding the idea, but in refining it.

Digital health technologies need to be designed and implemented with a keen understanding of the healthcare environment. Innovators should focus on addressing unmet clinical needs, facilitating integration into existing workflows, demonstrating cost-effectiveness, and backing their technology with robust clinical validation.

Taking a page from the successful adoption story of pulse oximetry, digital health needs to position itself as a solution, not a complication, in healthcare practice. As we refine these technologies, ensuring they’re attuned to the specific requirements of the medical field, we’ll likely see a significant acceleration in the adoption of digital health.

The adoption of digital health may not be about the speed, nor the idea itself, but rather how well the idea is tailored to suit the unique and complex world of healthcare. Just as with pulse oximetry, once digital health technologies align with these key factors, we could witness an adoption rate that matches or even exceeds the optimistic projections set for it. Let’s not give up on the idea – let’s refine and evolve it. The future of digital health is just around the corner, and it’s an exciting one!

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