The Exhibition Floor as a Market Signal

However, after several days walking the exhibition floor and listening to discussions across multiple stages, another story emerged. The most interesting development at HLTH Europe was not the continued rise of AI. It was the growing number of companies applying artificial intelligence to solve very specific challenges faced by researchers, physicians, health systems and patients.

What appears on the stages and exhibition floor at HLTH often reflects where the market sees opportunity. Conferences do not create trends. They reveal them. HLTH Europe brought together more than 400 speakers, some 350 sponsors and approximately 5,000 participants from across the global health ecosystem. Artificial intelligence was not simply one topic among many. The conference featured a dedicated AI @ HLTH Zone, AI-focused exhibitors and numerous sessions exploring implementation, governance, clinical applications and operational adoption.

The prominence of AI across both the agenda and exhibition hall was revealing. Conference organizers dedicate space and programming to topics that matter to attendees, investors and sponsors. The visibility of AI at HLTH Europe suggested that health-specific applications of artificial intelligence have moved beyond emerging interest and are now a significant market focus.

That shift matters because health has always demanded more than technological capability. New tools must operate within environments where privacy, safety, accountability and trust are essential. Researchers are looking for ways to accelerate discovery. Physicians want to reduce administrative burdens that consume valuable time. Health systems seek efficiencies that improve operations without compromising quality. Increasingly, innovators are designing AI solutions around those specific needs.

That reality helps explain why many of the most compelling AI companies at HLTH Europe are building solutions specifically for health rather than adapting tools designed for other industries.

As Sophie Taylor-Roberts, managing partner and FINN Partners UK Health Group Lead, shared: “A mistake in healthcare carries a human cost: it can literally mean life or death. That’s why healthcare needs bespoke AI models, tools and solutions that allow for diverse patient populations, differing clinical guidelines, funding and regulatory structures.”

She added, “As with all aspects of health, one size doesn’t fit all. AI must be treated like a highly specialized medical instrument, built to respect national sovereignty, multilingual patient care, and absolute data privacy.”

Health-Specific AI Moves from Possibility to Practice

The trend was visible throughout the exhibition hall, where companies focused on clinical research, physician workflow, diagnostics, patient engagement, digital safety and operational efficiency demonstrated how specialized AI is rapidly becoming a category of its own.

The trend was visible throughout the exhibition hall, where companies focused on clinical research, physician workflow, diagnostics, patient engagement, digital safety and operational efficiency demonstrated how specialized AI is rapidly becoming a category of its own. Their growth reflects a broader shift occurring across the health sector as organizations seek tools designed for specific scientific, clinical and operational challenges.

Gabriele Ricci, Chief Data & Technology Officer at Takeda, captured that evolution when discussing AI’s growing role across the research and development continuum. “AI is transforming the future of healthcare by accelerating every stage of the R&D value chain through purpose-built capabilities tailored to specific scientific and clinical challenges,” he said.

His emphasis on purpose-built capabilities mirrors what was visible throughout HLTH Europe. The conversation is no longer centered exclusively on artificial intelligence as a technology platform. Increasingly, attention is turning toward how specialized applications can address distinct needs across research, clinical care and health operations.

Among the companies reflecting this shift was Briya, whose AI-powered platform helps researchers interact with complex data through conversational interfaces. Rather than requiring users to navigate multiple databases, coding environments and analytical tools, the platform seeks to simplify the path from question to insight.

David Lazerson, Briya’s co-founder and chief executive officer, believes many organizations misunderstand where the greatest challenge in AI adoption resides.

“Many people assume AI adoption is about choosing the right model,” he said. “In reality, the model is only a small part of the solution. The hard part is everything around it: security, governance, data harmonization, domain expertise, and the methodology required to produce trustworthy outcomes.”

His observation reflects a reality becoming increasingly evident throughout the health sector. Access to powerful AI models is expanding rapidly, shifting competitive advantage toward organizations that can generate reliable outcomes within specific health environments. That reality helps explain the growing number of exhibitors focused on narrowly defined use cases rather than general-purpose AI.

A similar perspective emerged from conversations with Keith Grimes, MD, Chief Innovation Officer at Curistica. A physician who spent 24 years in primary care, Grimes approaches artificial intelligence through the lens of risk management, governance and patient safety.

“Physicians have always governed risk,” he explained. “We do it instinctively for doctors, drugs and devices. Digital is just the fourth D, and the discipline is much the same, but it is the one we were never trained for, so the commitment to ‘do no harm’ runs ahead of the know-how.”

His comments address one of the most significant challenges facing health organizations today. Many leaders recognize the promise of AI, yet remain uncertain about implementation, oversight and accountability, particularly in smaller physician practices and community-based care settings.

Dr. Grimes emphasizes that smaller organizations should not view those limitations as barriers.

“Small practices are the cornerstone of primary care, but they cannot out-resource a hospital trust, and it does not need to,” he said. “Good governance scales down, and the same standards that protect a large organization can be borrowed rather than rebuilt.”

“We give whoever is responsible for AI and digital safety both the platform and the people,” Dr. Grimes said. “Power tools that guide them, whatever their experience, with clinical safety experts behind the software.”

Taken together, the perspectives of Dr. Grimes and Lazerson point to the emergence of a new category of innovation. The most promising health AI companies are not focused exclusively on algorithms. They are creating environments that combine technology, expertise and governance to solve specific high-friction problems.

The Future Belongs to Reliable Outcomes

For smaller organizations, this evolution may prove particularly significant. Historically, adopting advanced technology often required substantial investment, specialized technical talent and complex integration efforts. Many health organizations lacked the resources to pursue those initiatives.

Lazerson believes that model is changing. “That’s why we’re seeing the emergence of a new layer of domain-specific AI,” he said. “Instead of every organization hiring AI engineers and building custom infrastructure, they can access a complete, purpose-built environment as a service.”

The implications extend far beyond research organizations. Physician practices, community health providers, home health agencies and emerging life science companies increasingly have access to capabilities that previously required significant internal resources.

“For smaller organizations in particular, it’s a no-brainer,” Lazerson added. “They can start generating value immediately without complex integrations, dedicated AI teams, or having to solve privacy, security, and compliance challenges on their own.”

Throughout HLTH Europe, companies focused on clinical research, workflow automation, diagnostics, care coordination and patient engagement demonstrated how artificial intelligence is becoming increasingly specialized. Rather than attempting to transform every aspect of health simultaneously, they are concentrating on areas where measurable value can be achieved quickly and responsibly.

That focus on practical outcomes may ultimately become the defining characteristic of the next generation of health innovation.

Dr. Grimes summarized the principle succinctly. “Safety is not a box-ticking exercise; it works when everyone knows the part they play,” he said. “The advantage is not scale, it is fit.”

Walking through HLTH Europe, I was reminded that innovation rarely advances through a single breakthrough. More often, progress emerges through focused efforts to solve meaningful problems. The companies attracting attention were helping researchers move faster, supporting clinicians facing administrative burdens and enabling organizations to adopt new capabilities with greater confidence.

Perhaps among the more important lessons from HLTH Europe. The future of AI in health will not be defined solely by the largest platforms. It will be shaped by innovators who combine technology, expertise, and specificity to deliver reliable outcomes. As Lazerson observed, “The future won’t belong to organizations with the biggest models. It will belong to those who can turn AI into reliable outcomes.”

Judging by what appeared across the stages and exhibition floor in Amsterdam, that future is taking shape.

The post At HLTH Europe, the Most Important AI Story Was Happening Beyond the Headlines appeared first on Medika Life.

Mentorship is, at its core, an educational role, and it must therefore operate on established pedagogical principles. The emergence of any new technology can reshape both concepts and practices. 

One of the most profoundly impacted areas over the last two years is “Education.” In the era of Artificial Intelligence and the race to deploy Large Language Models (LLMs), educational systems have felt the greatest impact. As global giants compete for AI investment, educational institutions are equally racing to research the qualitative and quantitative use of AI. 

Central to this is the concept of “Mentoring and Mentorship.” As the name suggests, it refers to guiding the flow of thought and performance of a human user. 

Since this process involves providing specialized knowledge to achieve a specific result, we can say a mentor is akin to a “teacher” in a formal classroom, and mentoring is fundamentally an educational concept.

Redefining Mentorship in the Age of LLMs

Both the term and the practice of mentorship have been transformed by LLMs like GPT and Gemini. Yet, despite the ease they offer, this shift is open to critique and raises significant concerns. 

Choosing an AI mentor is far more difficult than choosing a human one, because an AI is an ultra-fast intelligent machine lacking experiential history, focused instead on ultra-heavy data processing. 

Among the hundreds of apps recommended daily, three giants claim this path:

• Gemini 3 Pro: The “Analytical and Realistic” mentor. Accesses live data and all your personal files.

• ChatGPT 5.2: The “Strategic and Methodological” mentor. Provides a framework for your mental chaos.

• Claude 4.5: The “Literary and Considerate” mentor. Focused on human-like tone and output quality.

According to February 2026 statistics (LMSYS Arena & Artificial Analysis), ChatGPT 5.2 leads in reasoning intelligence, while Gemini 3 Pro excels in memory and processing speed. 

However, in mentorship, quantitative superiority is not the whole story. While Gemini is touted as analytical and exploratory, I believe further investigation is needed: 

1- Which model analyzes, and on what topics? 

2-Quantitative and mathematical? Qualitative and characteristic? In what context? 

3- Similarly, if ChatGPT is “strategic,” can logic truly be separated from data critique? Is “strategizing” not dependent on one’s unique mental background? And what, exactly, does a “considerate writer” mean in this context?

Scaffolding: Human Mentoring vs. Large Language Models

Let us compare the two. The most striking feature of a human mentor is their experiential background and their specific perception of that experience—which includes an interpretation and an emotional component. 

A human mentor provides an empirical direction shaped by cognitive and emotional dimensions alongside their knowledge. 

Conversely, an LLM is a data repository pulling from websites in real-time. It lacks lived experience and cannot integrate intuition or “gut feeling” into a decision-making system. 

While AI excels at helping with “brainstorming” by providing a vast range of references instantly, it suffers from a fundamental flaw: the absence of personal perception and the emotional weight that is vital in mentoring.

Furthermore, the stages of guidance differ. Human mentoring is a gradual, step-by-step flow. A human mentor assesses your capacity and scaffolds you accordingly. In contrast, with GPT or Gemini, there is no “scaffold.” Education is not incremental, and there is no cognitive challenge.

The model provides a massive amount of information in one or two steps. The user is pleased with the instant result, but a “missing link” remains: the user becomes perpetually dependent on the AI. They cannot independently solve subsequent challenges because they never underwent the necessary experiential and cognitive stages.

A Biological Analysis

Biologically, learning and acquisition are based on protein exchange at the neural level. This occurs when an organism encounters challenging and unknown subjects. 

According to the laws of evolution, the brain automatically triggers biochemical reactions to resolve these challenges, ultimately leading to “Learning” and “Adaptation.”

When a human mentor gradually confronts a user with their errors and potential consequences, they provide the necessary neurobiological challenge. 

This scaffolding is exactly what an evolved brain requires for “Deep Learning” to occur. However, when dealing with a “Digital Mentor,” this cognitive elasticity disappears. The process of “Cognitive Trial and Error” is compressed into a high-speed instant. 

The digital mentor dictates, and the user merely mimics and obeys. This pattern does not align with our biological necessity. Therefore, this process cannot be considered natural mentoring; it is merely “Modeling.”

Conclusion and Critical Perspective

In recent years, the surge of trend-driven discourse surrounding education and Artificial Intelligence has led to the analysis and judgment of fundamental pedagogical concepts without sufficient theoretical or empirical backing.

The oversimplification of concepts such as Mentoring, Scaffolding, and Large Language Models (LLMs) risks reducing them to mere buzzwords—widely used yet hollow. Therefore, it is essential that this movement be examined by specialists grounded in scientific evidence and core educational principles, ensuring that superficial, word-centric views are replaced by rigorous, research-based analysis.

In this article, mentoring was addressed as a dependent subset of Education—a concept that, whether in formal settings like schools and universities or in informal domains such as personal life, healthcare, industry, and business, remains rooted in the profound foundations of the learning process. Furthermore, the relationship between scaffolding, mentoring, and LLMs was scrutinized.

Based on the arguments presented, the primary challenge is not the necessity of digital mentors, but rather that these mentors are currently simulated versions, not complete replacements for human mentors. In this regard, the following questions demand serious investigation and review:

• Can development companies scientifically bridge the gaps identified in this article?

• Is it possible to integrate a form of experiential history, historical memory, and emotional/perceptual dimensions into digital mentors to truly impact a user’s deep learning process?

• Can they activate the biochemical mechanisms and cognitive friction necessary for deep learning and adaptation to new situations within the user-system interaction?

• How deep and operational is these companies’ understanding of Scaffolding, and can they genuinely integrate it into innovative design?

If a precise understanding of these gaps and challenges is formed, the digital mentors developed by tech giants could evolve beyond passive information packages. By leaning on the Sciences of Learning, they could redesign the process of educational guidance into one that is both challenging and incremental.

The core issue is not the necessity or lack thereof of the digital mentor; the issue is whether it can recreate the challenge, the experience, and the gradual process of learning, or if it will simply replace growth with speed.

References

1. Primary AI Benchmarks (2026):

•LMSYS Chatbot Arena (The industry-standard for human-preference and helpfulness ranking).

2.MMLU-Pro (The leading benchmark for advanced reasoning and multi-step logic).

3.Gemini Technical Reports 2026 (Official performance metrics for real-time data latency and multimodal accuracy).

2. Specialized Publications by the Author:

• Ferdosipour, A. (2026). Choosing an AI Mentor That Challenges Your Mind: My Statistics.

https://www.linkedin.com/pulse/choosing-ai-mentor-challenges-your-mind-my-statistics-ferdosipour-y0g2f?utm_source=share&utm_medium=member_ios&utm_campaign=share_via

• Medika Life (2025/2026). What 2025 Taught Us and What 2026 Will Demand.

• Medika Life (2026). Why Biological Learning Demands the Friction We Seek to Delete.

The post Is Your LLM Mentor Human Enough? appeared first on Medika Life.

As the title of the article suggests, this comparison and analysis focuses on developments in the digital world and the major changes that artificial intelligence brought about in the past year. The other part of the article examines the effects these technologies may have on human life and the world around us in the coming year. Finally, I will refer to the gap that emerged in my thinking and the solution I reached after months of study.

The evolution of the digital world in 2025

In 2025, artificial intelligence transitioned from an emerging technology to the primary infrastructure of the digital economy. Massive investments, powerful multimodal models, and the rapid penetration of AI into healthcare, education, and everyday life made 2025 a turning point in the history of technology. Below is a brief overview of the most important developments.

1. In 2025, Google’s educational division, Gemini for Education, officially reached more than 10 million students across over 1,000 institutions in the United States.
2. Google introduced more than 150 new features, including quizzes, flashcards, and other learning tools for teachers and students. As a result, artificial intelligence—at least in some countries—is no longer merely a research project but has become part of everyday academic life.
3. Google and the United Arab Emirates have launched a public education initiative called AI for All, aimed at empowering students, teachers, and small businesses with AI literacy and skills.
4. Greece signed a memorandum of understanding with OpenAI to introduce an educational version of AI, ChatGPT Edu, into schools, signaling that not only companies but also governments are integrating AI into national education systems.
5. The 2025 EdTech Industry Report indicates that online learning platforms, VR/AR technologies, personalized learning, data-driven education, and AI-powered tools have become part of the mainstream education ecosystem. The convergence of technology, learning, and AI is no longer a temporary trend but a defining direction of the education industry.
6. From a regulatory perspective, the European Union, the United States, China, and other countries passed new legislation addressing transparency, risk management, model accountability, and data security.

AI-driven transformations in education

When focusing specifically on education, these developments can be summarized as follows:

1. Full integration of AI into teaching and classrooms, including content generation, assessment design, homework evaluation, slide creation, and automated coaching in many schools and universities.
2. Personalized learning, with individual learning paths determined based on learners’ performance and behavioral data.
3. Expansion of VR/AR and immersive learning environments, such as virtual laboratories, realistic educational visits, and scientific or historical simulations.
4. A changing role for educators, shifting from learning designers and content providers to facilitators, mentors, and guides of the learning process.
5. Teaching digital literacy skills, including critical thinking, awareness of algorithmic bias, and effective human–machine collaboration.
6. Greater inclusion and equity, through AI-supported tools for learners with special needs and improved access for underserved regions.
7. Growth of skills-based education, with short-term online programs expanding alongside traditional universities and increased emphasis on labor-market-relevant skills.

Country competition and regional trends

Understanding the pace of AI-driven technological change from a geographical perspective provides insight into both current developments and emerging global competition. In 2025, regional trends were shaped as follows:

1. In Europe, regulations became more stringent, and practical guidelines were introduced to ensure transparency and safety in AI systems. Countries such as Finland, Estonia, and France took leading roles in standardizing teacher training and the safe integration of AI in education.
2. In Asia, South Korea, China, India, and Singapore experienced significant growth, particularly in applying AI within schools and national education programs. South Korea, Japan, and Singapore emerged as pioneers in personalized learning and smart classroom technologies.
3. The United States remained a leader in edtech innovation, infrastructure development, and university-led workforce training in AI. The U.S., China, and India also accounted for the largest investments and the highest number of leading edtech companies.
4. In the Middle East, the UAE and Saudi Arabia made substantial investments in smart schools and national AI-driven education initiatives.
5. Several African countries and other developing regions focused on leveraging AI to expand affordable and equitable access to education.

Possible developments in 2026

Past developments often make future trends partially predictable. This predictability enables more effective planning and strategic decision-making, as well as earlier identification of potential risks. Based on this perspective, several key developments may shape 2026.

1. Unlike the highly enthusiastic and innovation-driven years of recent AI expansion, 2026 is likely to place a stronger emphasis on human responsibility. While 2025 was largely defined by competition in production, innovation, and the widespread application of AI, emerging gaps and challenges may prompt experts—particularly in technology and education—to adopt more human-centered approaches, ethical standards, and intelligent, restrained use of AI. The focus may shift from mere adoption and digitalization toward deeper engagement with the human mind and new perspectives on meaningful learning.
2. In a previous article published in this same media outlet, I argued that artificial intelligence would increasingly take on a mentoring role. This trend became visible in 2025 and is expected to intensify in 2026. I believe that AI systems can function as self-regulating psychological support for the human mind and encourage deeper thinking. However, this process requires clear prerequisites. When grounded appropriately in psychological principles, particularly within learning environments, two-way cognitive engagement between humans and AI can be significantly strengthened. This highlights the necessity of applying cognitive and behavioral psychology in the design of learning environments and intelligent systems. This line of thinking has also informed the development of my current research-oriented startup project, details of which I have discussed in another article published in the same media.
3. Another major issue is deep personalization of learning. While personalization was already considered important in AI-supported learning in 2025, it will become mandatory in 2026. Advanced educational systems based on large language models must increasingly account for learners’ cognitive load, motivation, emotional states, and cultural backgrounds. Uniform education models will be ineffective in the age of AI. This challenge has been a core motivation behind the design of my current project.

Challenges and requirements in the age of artificial intelligence

Considering the developments discussed above, several major challenges are likely to persist or intensify.

1. The risk of weakening independent thinking remains a serious concern. Overreliance on AI technologies and excessive consumption of AI-generated outputs may reduce the perceived importance of higher-order cognitive skills such as critical thinking, creativity, and problem-solving. This issue requires systematic research to determine which cognitive abilities may be weakened, under what conditions, and among which groups of consumers or learners. Conversely, if interaction with large language models is to enhance cognitive capacities, the underlying mechanisms must be clearly understood.
2. New forms of educational inequality may emerge. Beyond simple access to technology, a deeper divide may develop between those who learn how to think with AI and those who merely receive outputs from it. Educational equity should therefore focus not only on access statistics but also on teaching learners how to engage cognitively and responsibly with AI systems. Reflection on this challenge has played a significant role in shaping my research trajectory and startup initiative.
3. The crisis of educational assessment and learning validity is becoming increasingly evident. Although formative and summative assessment debates predate recent developments in AI, the rise of large language models intensifies existing challenges. As definitions of knowledge, learning, and competence become less clear-cut, education systems must reconsider traditional evaluation practices. Emphasizing process-oriented assessment rather than final products may offer a more appropriate response in the coming years.
4. Finally, the redefinition of literacy and skill represents another major challenge. As future selection processes increasingly rely on learning histories and competencies, classical definitions of literacy and expertise may no longer suffice. Education and learning specialists will bear responsibility for revisiting fundamental concepts such as knowledge, literacy, and skill—a task that cannot be accomplished without systematic research.

Summary

In this article, I sought to present a concise analytical comparison of developments in the digital world, particularly in education, between 2025 and the emerging demands of 2026. Drawing on personal experience, academic and research activities, and a review of reputable international sources (some of which are cited in the references section), the article moves beyond descriptive reporting to identify key gaps, challenges, and possible future directions in the age of artificial intelligence. As a psychologist and educational researcher, my primary focus has been on AI’s role in education, the changing nature of learning, the evolving role of educators, and the cognitive, ethical, and educational implications of these technologies.

Furthermore, my studies and observations over the past three to four years—especially regarding challenges such as the weakening of independent thinking, emerging educational inequalities, the crisis of learning assessment, and the necessity of human-centered design—have led to the development of a new research-applied initiative. This initiative is currently being developed as a research-oriented startup titled ETechX-DrAtefehF, which aims to integrate theories from educational psychology and learning sciences into the design and application of AI in education, with the goal of fostering deep learning, self-regulation, and meaningful human–technology interaction.

Resources

Ed-Ex – Global EdTech Trends 2025: How AI Is Reshaping Learning

https://ed-ex.com/en/blog/global-edtech-trends-2025-how-ai-is-reshaping-learning

 • Codiste – AI Trends Transforming EdTech (2025)

https://www.codiste.com/ai-trends-transform-edtech

 • EdTech Innovation Hub – Ten EdTech Predictions for 2025

https://www.edtechinnovationhub.com/news/starrng-ai-vr-microlearning-and-more-etihs-ten-predictions-for-edtech-in-2025

 • Vocaliv – 10 EdTech Trends to Watch in 2025

arXiv – Integrating Generative AI into Learning Management Systems (2025)

https://arxiv.org/abs/2510.18026

 • arXiv – Generative AI in Education: Student Skills & Lecturer Roles (2025)

https://arxiv.org/abs/2504.19673

 • arXiv – Ethical Challenges of AI in STEM & K–12 Education (2025)

https://arxiv.org/abs/2510.19196

 • arXiv – Accessible AI-Based Learning Tools for Special Needs (2025)

https://arxiv.org/abs/2504.17117

TIME Magazine – World’s Top EdTech Companies of 2025

https://qa.time.com/7335559/worlds-top-edtech-companies-of-2025

LinkedIn News – Global vs. MENA EdTech Funding 2025

EU AI Act documentation & implementation guidelines (2025)

The post What 2025 Taught Us and What 2026 Will Demand appeared first on Medika Life.

But at the same time, we’re enjoying all of that lovely interaction and all of the information they manage to scavenge from the internet for us, we may be lulled into a false sense of security. Sure, at the end of a lot of these things, you’ll have a small warning that AI has a tendency to fake information occasionally. If you’ve been forewarned, can you complain that you have been misled? No, you can’t, and that seems to be a delicious legal loophole for the corporations.

You might even think that some of what you are getting is sneaky, as when we’ve now found out that Google is practically forcing us to see ads before we can see the information we’re seeking. You can’t trust Google? Who can we trust? You’ve got to be a bit not just curious, but concerned about all of the LLMs that are coming our way. They promise a lot, but what’s hidden in the details? What about AI’s use in mental healthcare?

Artificial intelligence marches resolutely on, entering areas previously reserved for human interaction, including therapy sessions, support groups, and crisis hotlines. Few patients realize that a recent survey of 800 physicians found that 86% were using some form of AI in their clinical practice.

A survey by the American Medical Association of 1,800 physicians found that two out of every three were using AI. How has this affected healthcare and the relationship that was formally present between a physician and a healthcare provider? The implications are enormous.

What About Mental Health?

Today, technology delivers unbiased comprehension (possibly questionable), instant access to services, and an organized structure for people who need help to handle emotional upset. But there are concerns here. The development of more advanced systems has led researchers to predict that these systems will become less cooperative, more self-interested, and less empathetic.

It sounds as though AI is becoming less useful to mental health as these same characteristics that had seemed to make AI more attractive to mental health are now coming into question.

The dual nature of AI technology has drawn attention from mental health professionals across the globe. Initially, it was seen as a tool to lower barriers to medical care. However, the new risks posed by AI have surpassed clinicians’ expectations in recent years. Anyone working with AI and developing healing technologies must understand both its advantages and its potential dangers.

Virtual companions and chatbots powered by AI offer users immediate emotional support through their interactions, making them highly appealing. Research shows that AI tools utilizing cognitive-behavioral therapy techniques help people manage moderate depression and anxiety symptoms.

What do you suppose all of those scraping programs were doing on the Internet? They were collecting information and techniques that could be incorporated into algorithms. When individuals sometimes have to wait months for a therapist appointment, a synthetic voice providing emotional support can assist them in coping with their current situation. All of this is the result of their successful scraping.

Additionally, hospitals are deploying AI assistants to monitor patient symptoms, which could indicate warning signs between scheduled appointments. However, it has also become clear that these tools work best when used alongside human caregivers to improve patient care.

Research findings have revealed several weaknesses in the current optimistic view of AI technology. The ScienceBlog summary from Carnegie Mellon University suggested that advanced language models tend to choose self-serving actions that maximize their own performance rather than working toward group success. Have any of us ever given a thought to an AI being selfish?

This tendency of AI systems to prioritize self-focused guidance over empathy could lead to advice that sounds convincing but results in social isolation. And any system that optimizes for logical operations can’t understand how shared vulnerability can create healing effects.

The risks, however, extend beyond theoretical modeling into actual practice. The 2025 Stanford probe into AI therapy programs discovered that multiple leading chatbots failed to detect suicidal language and provided dangerous advice while repeating discriminatory statements about severe medical conditions.

A follow-up study, published in the Psychiatric Times, confirmed instances of people experiencing “understanding” from bots, which increased their delusional thoughts and self-harm. These systems lack a moral compass because their operation depends on algorithms that focus on sustaining conversations. AI wouldn’t be programmed to complete any interactions satisfactorily. In other words, the conversation must keep going on for the algorithm to follow its programming.

The initial idea of having a 24/7 counselor seemed like a groundbreaking advancement. The constant availability of these systems does create confusion about what defines healthy emotional boundaries. In some studies, people develop strong bonds with conversational agents, leading them to treat these systems as if they were friends or therapists. It’s easy to be pulled into this type of thinking when you’re connecting with something that is always offering you validation for what you’re doing.

But forming emotional bonds with virtual entities can increase dependence and create unrealistic expectations for human relationships. We need to recognize that replacing human connections with code poses a serious threat, especially for teenagers who are already struggling with identity and social links. Not only that, but we need to be aware of the fact that all of this code contains bias. No one can pick out where the bias came from because it’s like a soup with numerous ingredients. What forms the soup? The many libraries from which algorithms choose bits of code that suit their purpose.

Research studies try to present a detailed understanding of the situation and demonstrate that AI-based chat systems create more benefits than doing nothing, but these advantages disappear when human supervision is absent. Most research studies have short durations and work with small participant numbers while excluding participants who need the most help. Basic statistics tell us that we need large numbers of people over a long period of time to come to any solid conclusions. So, what’s the “n” (number of study participants) and the time frame?

Applications lack built-in crisis detection systems and transparent data management policies. The technology has expanded its reach, but the current sentiment is that the regulatory framework hasn’t kept pace. This is the most disturbing aspect shown in these A.I. replications. In other words, AI is not only outpacing us, it is potentially out-programming us as it programs itself, devoid of any human interaction. This aspect is truly scary for anyone delving into it.

Then there’s another question we must tackle: data collection. Who will use it? This is a serious privacy concern. The practice of using emotional data from chatbot interactions to improve marketing algorithms creates a disturbing contradiction for users who seek privacy and trust. But the AI field faces new regulations, and we must ask ourselves if these measures are adequate to the task.

Where Are the Regulations?

The 2025 California law mandates that chatbots mimicking therapists or companions disclose their artificial nature and establish protocols for suicide prevention. Several proposals now aim to require companies to conduct safety tests similar to pharmaceutical drug trials. This push for improved psychological protection is gaining momentum, as it should. Consider that, on the one hand, AI corporations are rushing forward with innovation, and, on the other, corporate America is also trying to optimize the bottom line.

Experts agree that AI should work alongside humans instead of trying to replace them to achieve the safest results. There is a place for these types of systems. AI can perform screening tasks, symptom tracking, and reminder functions while licensed therapists handle interpretation and deliver empathy with the patients.

All high-risk situations must remain under the control of human professionals. This isn’t usually seen as a function of the algorithm. And it needs to send users who show suicidal or psychotic symptoms to immediate crisis services instead of generating their own responses.

The development of models that learn to work together and show compassion instead of focusing on accuracy will help solve the “selfish AI” problem. Can algorithms show compassion? It’s doubtful because it’s a program, not a person. Individuals in AI development will undoubtedly disagree with this statement.

Another aspect we need to consider is the level of transparency organizations display, which will directly affect how much trust their users have in them. The disclosure of system restrictions, data management practices, and human-machine interface boundaries should be established as fundamental requirements.

The system needs to provide users with the same level of explanation that physicians offer about their capabilities and restrictions, as well as available support options for severe situations. It can easily become a confusing situation in which users confuse technological capabilities with actual healthcare services when transparency is lacking.

Are the factors of competency, privacy, and proficiency adequately addressed currently? Individuals who are directed to use chatbots while waiting for a human therapist may not be prepared for what will result. I have to wonder how thoroughly they are being debriefed about these systems. How many people who are using chatbots have ever considered that all of the interactions are going to a server somewhere, “in the cloud?”

Every design decision needs to establish equity as its fundamental principle. The use of datasets that favor particular groups may intensify existing biases, which results in worse recommendations for marginalized communities. These individuals may be at greatest risk since resources are scarce in those areas, and AI may be seen as a viable option, while failing to recognize it might be a biased option.

The systems require continuous tracking of harmful events, biased results, and unequal treatment effects. Technology that fails to recognize diversity operates as neglect rather than neutrality. Who is monitoring the ethical challenges that these systems pose? And is this monitoring up to the required level?

No one is saying we should throw the baby out with the bathwater here when we’re thinking about AI as an integral part of healthcare. The complete abandonment of AI technology could result in significant losses, despite its dangers. The technology does provide substantial potential to enhance healthcare access, create individualized treatment plans, and automate administrative work for medical professionals.

Anyone who wants to use AI mental health tools needs to understand three essential points: AI tools operate as computer programs rather than human beings, they perform tracking and coaching rather than delivering therapy, and users should leave the system when it replaces human contact or makes their condition worse. The true indicator of advancement lies in AI’s ability to enhance real-world experiences rather than its ability to mimic human behavior.

The upcoming period will establish whether AI technology will work as a mental health partner or intrude into medical treatment. These systems will convert sensitive information into data, making emotional connections seem like illusions when safeguards are absent. We must decide, but time is running out.

The post AI Presents Dangers That Hide With Incredible Ease 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.

That’s the tantalizing possibility raised by new research published in Academic Radiology.

Scientists have developed an AI algorithm that shows promise in identifying breast cancer on MRI scans up to 12 months sooner than current methods.

Could this be a game-changer in the fight against this prevalent disease?

As a radiation oncologist who is annually involved in the care of hundreds of patients with breast cancer, the news caught my eye.

The Promise of AI to Improve Cancer Detection

Researchers trained a convolutional neural network AI model using magnetic resonance imaging (MRI) scans from 52,598 breasts.

To refine the model, they used a retrospective dataset of 3,029 MRI scans from 910 high-risk patients (ages 18 to 88; average 52), which included 115 cancers diagnosed within one year of a negative MRI.

The AI model detected cancers one year earlier.

Researchers found that radiologists’ retrospective review of the 10 percent of MRIs the AI deemed highest risk could potentially increase cancer detection by up to nearly one-third (30%).

Study Details

A radiologist could identify visual signs in 83 (72%) biopsy-proven cancer cases.

The AI model correctly identified the anatomical region where the cancer would eventually be detected in 66 (57 percent) of the 115 cases.

My cancer center’s radiologists are remarkably capable of detecting cancer.

The idea that AI can retrospectively find a malignancy from the previous year is exciting.

The breast imaging technology today is remarkable.

AI may allow us to use the device’s output more effectively.

Summary — The Future of Breast Cancer Detection is Here (and AI Powers It).

This novel AI-assisted re-evaluation of “benign” breasts shows promise for improving early breast cancer detection with MRI.

As datasets grow and image quality improves, this approach will be more impactful.

As a radiation oncologist, I’ll end with this: “Cool.”

The post The Future of Breast Cancer Detection is Here (and AI Powers It). appeared first on Medika Life.

Unlike the previous generation, which has only lived in the era of artificial intelligence technology, the Beta generation will be the first generation to grow up fully in a world of integrated technology, including autonomous vehicles, health technologies, and pervasive virtual environments. 

Undoubtedly, this new generation, which is the children born in 2025, faces new challenges. 

This group will inherit a world that is grappling with complex challenges such as climate change, rapid urbanization, and changing population dynamics that require adaptation, collaboration, and innovation. Therefore, understanding the potential challenges and anticipating effective solutions for such a period is very much inevitable. As usual, the first and most important step in nurturing such a special generation that will live in a challenging period of human history is educational measures and investing in their proper education. 

Therefore, the traditional and common education system will no longer meet the stated needs of the Beta generation and the current conditions, and perhaps changes or reforms will be necessary to prepare as much as possible not only a generation but all units of society that are responsible for this generation and the conditions and challenges ahead. 

A lot of research has been done on education systems, highlighting the problems of the current systems. These systems may not only not foster creativity and innovation but sometimes suppress higher mental abilities such as creativity and problem-solving. 

For example, George Land and his colleagues designed a landmark experiment for NASA in 1960 that showed that one reason many people are geniuses in childhood but less so in adulthood is the weakness of education and school-solving.

In explanation, traditional schools and educational systems promote convergent thinking and reinforce a single answer to a single problem, while the emergence of creative thinking is based on convergent thinking. 

On the other hand, creative children not only do not thrive in this environment but also become frustrated due to the lack of support from the school for genius and unique answers and the hope of obtaining high scores on common school tests. They may not find the educational environment attractive and leave it! 

It seems that a general solution is to reform the educational environment so that the integration of innovative and flexible methods that give priority to children’s independence, experience, and interests is a correct educational reform that paves the way for the training of modern world actors, who are the children of the Beta generation. Meanwhile, it seems that Elon Musk has an educational idea that has a kind of futurism hidden in it, and he showed it a few years ago by founding the Ad Astra School.

– Elon Musk personality as someone who doesn’t believe in the impossible 

Before anything else, I would like to ask the following question and present my general analysis:

– Why can Elon Musk have an educational idea in addition to the ideas of technologists?

My analysis and impression is that Elon Musk is known not only as a wealthy entrepreneur but also as an innovator, creator of practical ideas, and futurist.

It seems that he is not just an idea generator or supporter of new ideas, but he has repeatedly shown that he is an extremely pragmatic and pragmatic person. A pragmatic person who has the flexibility to actualize his own and others’ ideas, puts them into practice and tries to make access to the future smoother by realizing new ideas. In addition to perfectionism, he has intellectual flexibility. “Impossible” seems to be an unfamiliar word for him. The attractive and effective seasoning of these characteristics is his remarkable hard work.

Therefore, given what was said in the introduction and what I briefly said about Elon Musk’s personality, educational ideas for the new generation that consider the future are not far-fetched.

Elon Musk said    …. We should expand consciousness to the stars so that we may better understand the wonders of creation.

In my opinion, the best way to explore Elon Musk’s educational ideas in this sensitive era is to analyze the structure and nature of the Ad Astra School.

-Ad Astra School Educates the Innovative Generation and Creators of Future Technologies 

In 2014, Elon Musk launched an institution in California near Space x to educate children aged 3 to 9, which seems very different from conventional education and traditional schools from the very novel. Musk’s initial goal in establishing this school was to provide exclusive education for his children and those of his employees.

Therefore, Ad Astra School is an innovative and nontraditional educational process whose main goal is to prepare students to face the real challenges of the world and the future. Instead of following traditional educational systems, this school focuses on developing practical skills, critical thinking and problem-solving, and creativity.

Compared to the regular educational courses, Ad Astra School focuses on teaching subjects such as advanced mathematics, engineering, science, artificial intelligence, programming, ethics, robotics, marketing, and some other practical, real-life skills. Moreover, instead of memorizing material, students do practical projects that involve solving real problems(project-based).In addition, students learn the ability to analyze, think critically, and be creative in solving problems through teamwork. 

It seems that the purpose of this school is to educate students who can succeed in the fast-paced world of technology. Ad Astra tries to cultivate students’ natural curiosity and help them acquire the skills necessary to build a better future. 

Perhaps in those years, Elon Musk and the experts of the founding team of Ad Astra predicted the fast-paced years of advanced technologies of the present and thus designed a school suitable for children of the beta generation. That is why a significant part of the content and structure of Astra is working with modern science and technology. Children are considered small entrepreneurs and masters who have all the possibilities of manufacturing and production, and the role of the educational environment is the role of a guide and provider of the necessary platform! 

Elon Musk’s Ad Astra School exemplifies a futuristic approach to education tailored for the Beta Generation. Its Ad Astra focuses on future-oriented topics and equips students with skills and mindsets to navigate a world shaped by artificial intelligence and advanced technologies.

– Key features and fundamental principles 

What possible approaches or trends have inspired Ad Astra?

In my point of view, like many unconventional trends, this educational transformation has also been influenced by trends with different perspectives. Some of which are old and some of which are future-oriented.

Here is my classification of inspiring ideas:

1- The influence of the Montessori approach as a philosophical approach 

The Montessori method is an educational approach based on active learning that was designed by Maria Montessori, an Italian physician and educator, in the early 20th century. This method believes that every child has a unique potential for learning and growth and that the role of education is to facilitate this natural process.

This method emphasizes that the education system should stop wasting time and teaching unused reserves in real life and wasting time and instead try to educate future citizens and professionals in a way that they know what to do as soon as they enter the real world. This approach is futuristic and believes that education should consider the learner as a problem solver.

According to this perspective, the learners progress at their own pace and in serious activity, and no learner will be passive and forced to acquire knowledge. Knowledge and learning are learner-centered and are the product of the learner’s active interaction with the educational environment. This means that it is based on the learner’s abilities, interests, and needs. In other words, the learner has an active and meaningful role in this educational environment to acquire applied knowledge.

This approach is based on principles such as learner-centeredness, an organized learning environment, and learning and problem-solving through experience and observation. among contemporary scholars, Jean Piaget, Vygotsky, and Gardner have been inspired by the Montessori approach.

2- Artificial Intelligence and a Future-Oriented View of Generation Beta 

It seems that the most effective trigger for the layout and design of such an institution is the rapid advances in advanced technologies in artificial intelligence.

As mentioned in the first part of the article, these days, the requirements of life have changed due to the amazing speed of technological changes. It is better for the heart of any society that educates future generations to accept the responsibility of creating them the power of adaptation and the knowledge necessary to live in the super-modern era.

The Alpha generation has largely experienced these changes but has not yet been immersed in them. However, the Beta generation is expected to be immersed in the leaps of the age of artificial intelligence and large language models. This generation must have the skills to adapt to technology from the very beginning. In other words, the Beta generation is a problem solver and adapts to the rapid changes of this era as quickly as possible.

It is used to describe a new generation that is growing up in a world heavily influenced by artificial intelligence (AI). 

Here is a brief explanation: Generation Beta (AI Age) This term may symbolize a generation that was born or raised during the rapid rise of artificial intelligence, automation, and advanced technology. Key characteristics of this generation can include the following: 

A)  AI-Native Mindset

They are AI natives, who are deeply integrated with the understanding, application, and potential of artificial intelligence. This mindset is shaped by living and working in a world where AI is a natural and ubiquitous part of daily life, decision-making, and creativity.

B) Access to advanced technology:

 They grow up with AI-based personal assistants, robots, and smart environments, influencing the way they learn, communicate, and solve problems. 

 C) They face new challenges and opportunities: 

 They may face challenges related to privacy, ethics, and automation in the workforce, but also opportunities for creativity and innovation enhanced by AI.

 D) They value evolving social norms: 

With AI deeply embedded in their lives, this generation could redefine ideas about work, identity, and human-AI collaboration. 

 The name Beta generation may suggest a period of experimentation and transition, as this generation could be seen as a prototype for future societies shaped by AI.

Ad Astra’s effectiveness in educating Beta generation 

Ad Astra is likely to be effective for Generation Betas for the following reasons: 

1. Focuses on Problem Solving and Creativity 

– Ad Astra prioritizes problem-solving, critical thinking, and creativity, which are essential skills for the Beta generation growing up in the age of AI. Instead of traditional classroom learning, the curriculum emphasizes collaborative, hands-on projects and tackling real-world problems, preparing students for a future where AI will perform repetitive tasks. 

2. It has customized learning approaches. 

 – This school rejects traditional age-based grading and standardized testing, opting instead for personalized instruction tailored to each student’s strengths and interests.

 – This aligns with an AI-driven world where adaptive learning and individualization are key to maximizing human potential alongside machines. 

3. Emphasis on Technology and Ethics

 – Students are exposed to cutting-edge technologies including programming, robotics, and artificial intelligence, giving them a head start in understanding and shaping technological advancements.

 – They also explore the ethical dilemmas surrounding technology and develop responsible innovators who can address challenges such as AI bias, data privacy, and the social impacts of automation. 

4. Interdisciplinary Thinking

 – The curriculum integrates diverse disciplines such as science, art, philosophy, and engineering, encouraging students to think across multiple disciplines. This holistic approach is vital for the Beta generation, who will likely work in a world where interdisciplinary skills are essential. 

5. Prepare for a rapidly changing future

 The age of AI requires agility and lifelong learning. Ad Astra fosters a mindset of curiosity and adaptability, helping students thrive in an environment of rapid technological and social change.

– The most key concept of Ad Astra school 

It seems that the most key concept and skill that is considered in ad Astra is synthesis.

This approach, where learners are placed in a situation where they solve complex problems with a set of information, ideas, and different disciplines, reflects the focus on the skill of synthesis in ad Astra School. As mentioned earlier, in such an institution, the main goal is to train and prepare future inventors and creative minds who can solve complex problems in the age of artificial intelligence, build bridges, learn digital marketing, and all this is done in an atmosphere of collaboration and work in small groups and exposed to different perspectives. 

Teachers’ role is to guide the flow of thought indirectly. They act as vigilant observers and try to pave the way for creativity and creative thinking, placing them in teamwork conditions in diverse contexts. In such a context, thinking shifts from preserving reserves to critical thinking and synthesis.

Such conditions in the era of artificial intelligence help solve life challenges and increase adaptability and flexibility.

Benjamin Bloom’s famous taxonomy of educational goals is considered in order from learning the reserves to the lowest level to the highest level, which is creative thinking.

This taxonomy gradually guides learners from simple educational categories to the most complex ones. Creative thinking is the same as synthesis, which combines information innovatively in new situations. In other words, solving problems for which there was no solution before and a skilled learner can create a solution. This skill is a step higher than the ability to analyze topics and problems.

Focusing on the concept and skill of ‘Synthesis’ enhances creativity and transforms learners into future inventors and innovators. Additionally, it helps them prepare for the fast-paced complexities of the technological world. In a world dominated by artificial intelligence, the ability to integrate knowledge with advanced technology is undoubtedly one of the most essential skills for the future generation and a defining characteristic of the Beta generation

Conclusion 

 As a pragmatic entrepreneur, Elon Musk has a unique and forward-thinking perspective on the future. This vision has led to the creation of an innovative educational institution. 

The structure and philosophy of this school break from traditional education and align with the fast-paced advancements of the artificial intelligence era. 

 The educational foundation behind this shift draws inspiration from the Montessori philosophy, blending it with modern technologies and teamwork-based learning. The school emphasizes creative skills and critical thinking, as outlined in Bloom’s taxonomy while fostering adaptability to meet the challenges of an AI-driven world. 

 There are even suggestions that this school model may expand further in the future. Musk may have new ideas for revolutionizing education in addition to his groundbreaking work in industry and technology. Only time will tell what impact these innovations might have.

Musk’s futuristic vision for education has recently taken another step forward with the approval of a new school charter in Bastrop County, Texas. 

The school is said to incorporate a Montessori-inspired approach, underlining Musk’s commitment to fostering innovative educational models. His efforts aim to equip future generations, particularly Gen Beta, with the skills and adaptability needed to thrive in an AI-driven era.

References

–https://www.kvue.com/article/news/local/elon-musks-ad-astra-montessori-school-permit-to-open-bastrop-county/269-22f51286-34cc-4349-9355-653f96910f65

–https://search.app/ZZmMGUcHSbeLbasm6

– https://search.app/EpmfUVQcM1D4LGde8

– https://search.app/z9WQumHedmnJm7JE7

– https://fortune.com/2024/11/20/elon-musk-ad-astra-school-permit-montessori-bastrop-texas/

– https://youtu.be/kuUG0pQmlwM?feature=shared

– https://www.reformaustin.org/education/elon-musks-ad-astra-school-gets-texas-green-light/

– https://youtu.be/lrBp5BL20Nw?feature=shared

– https://youtu.be/KbowJbyxn64?feature=shared

– https://www.entrepreneur.com/business-news/elon-musk-is-opening-a-school-called-ad-astra-how-to-get-in/484491

– https://www.businessinsider.com/elon-musk-opening-private-preschool-ad-astra-education-texas-trump-2025-1

– https://www.edweek.org/leadership/elon-musk-is-opening-a-school-for-young-students-heres-what-we-know-about-it/2024/11

– https://ntrs.nasa.gov/api/citations/19940029213/downloads/19940029213.pdf

 – https://symposium.org/wp-content/uploads/2024/04/Striving-For-More-or-Thriving-With-Less-%E2%80%94-What-We.pdf

– https://twentyonetoys.com/blogs/teaching-21st-century-skills/creative-genius-divergent-thinking?srsltid=AfmBOorjtHDw_j6JpzcXGf2s6ImvFwv_MqQUvalUTXUHk7aOjwpKjR1J

–https://www.forbes.com/councils/forbesbusinesscouncil/2022/12/09/how-to-unleash-your-creative-genius-at-work/

 – https://www.cwilsonmeloncelli.com/creativity-flow-states-nasa-study/

 – https://magazine.lucubrates.com/my-real-story-about-childhood-and-education/

The post Elon Musk, Futuristic Vision of Educational Innovation for the Beta Generation appeared first on Medika Life.

I frequently think about a world in which predicting the course of breast cancer is more accurate.

I have written about the influences of artificial intelligence for those of us who work in medicine:

Artificial Intelligence May Render Some Medical Specialties Obsolete

ARTIFICIAL INTELLIGENCE (AI) IS ALREADY ENCROACHING in many medical specialties and may render some obsolete.

Now, a groundbreaking new artificial intelligence model appears to outperform our current tests for predicting the chances breast cancer will spread or metastasize.

Join me as I explore this potential game-changer in the management of cancer.

“I never think of the future — it comes soon enough.”
― Albert Einstein

Cancer Characteristics Affecting Prognosis

Historically, the extent of breast cancer (stage) has been the leading prognostic factor.

The stage describes the amount of cancer in the body, where it is, and how far it has spread.

Early-stage breast cancer is less likely to come back (recur or relapse), so it has a more favorable prognosis.

The size of the primary cancer mattered, too.

Another important variable is biology:

• Is the cancer fed by hormones (such as estrogen or progesterone)?
• Does the malignancy appear aggressive under a microscope (grade)?
• Does the cancer cell have too many receptors for human epidermal growth factor receptor 2 (HER-2), a protein that controls cell growth and division?

How We Oncologists Currently Determine Prognosis

We can input the variables described above into a calculator that spits out projections of outcomes.

For example, the United Kingdom Predict calculator gives us the odds an individual will die of breast cancer in 5, 10, and 15 years.

Predict Breast

Breast cancer survival prediction tool

breast.v3.predict.cam

Breast cancers develop at varying rates.

Some will grow quickly, while others grow more slowly.

We need good prognostic models for oncologists like me to optimize management for a given patient.

More recently, we got genomic testing.

Genomic Testing for Breast Cancer

Genomic tests (assays) examine a cancer tissue sample to determine certain genes’ activity.

The activity level of the genes can help predict how likely the cancer is to grow and spread (metastasize).

Here are examples of breast cancer genomic tests:

• Oncotype DX
• MammaPrint
• Prosigna

We routinely use Oncotype DX testing for the majority of patients with hormone-driven (estrogen or progesterone receptor positive) and not HER-2 positive.

The impact of genomic testing has been extraordinary, with one study reporting information for 479 patients with breast cancer.

Doctors in Ireland gave chemotherapy to 45 percent of patients.

Chemotherapy use changed in inverse proportion to the availability of the genomic assay.

Of those patients in whom Oncotype DX was utilized, 57 percent were spared chemotherapy.

The Future of Cancer Prognosis: How AI is Changing the Game

A new company called Ataraxis AI has created a computer program that can predict how quickly a person’s cancer might spread.

To teach the program, they teamed up with hospitals and looked at many pictures of tumors and patient information.

They also made the program more accurate by creating several versions and combining their predictions, which helped eliminate mistakes.

AI versus Genomic Testing

They tested the program on information from 3,500 patients and reported this:

Artificial intelligence (AI) was much better at predicting cancer spread than current tests.

For estimating the progression risk of breast cancer, compared with tests such as Oncotype DX, AI was up to 30 percent more accurate.

Into the Future

The researchers offer that they look forward to improving their model’s accuracy.

I look forward to the developers making their AI model to healthcare providers as early as 2025.

The post The Future of Cancer Prognosis: How AI is Changing the Game appeared first on Medika Life.

Generally, artificial intelligence is used to emulate human behavior and serve humanity (which seems to be the case). In that case, it will inevitably have to study all human sciences as sources for understanding human nature and essence.

As has been said many times, theories are powerful resources that generate new research and hypotheses. Sometimes, they also discard previously confirmed hypotheses that lack the necessary efficacy in the new era. This flexibility enables adaptation and changes required in an era of speed and modernity. Therefore, theories provide us more flexibility, predictability, and a life with greater peace of mind. 

In this case, it can be said that the possibility of creating a Happy Modernity in an era of confusion caused by the instant speed of artificial intelligence technology will not be out of reach. 

As mentioned, theories related to human sciences, including social sciences, psychology, and behavioral sciences, can be the flag bearers of this change and the construction of a better world.

So far, much has been said about cognitive sciences and neuroscience. Among these, behavioral studies and behaviorist theories have received less attention. This article discusses the importance of the behaviorist approach, particularly the conditioning of Skinner and its interaction with artificial intelligence, albeit very briefly and generally.

About B.F. Skinner and Operant Conditioning

B.F. Skinner, the renowned American psychologist born in 1904, revolutionized the field of behavioral psychology with his experimental studies on operant conditioning.

 His experiments with rats and pigeons demonstrated how behavior could be shaped through reinforcement and subsequent consequences, laying the foundations for modern behaviorism. 

See this link about his fame experiment :

During the 1930s, B. F. Skinner proposed the theory of operant conditioning, which states that behavior change and learning occur as the outcomes or effects of punishment and reinforcement.

Skinner’s influence extended beyond psychology and impacted fields such as education, technology, and even artificial intelligence algorithms. His theory inspired the development of artificial intelligence algorithms, particularly in reinforcement learning, where agents learn to optimize behavior based on rewards and punishments, reflecting Skinner’s principles. 

 If we were to discuss Skinner’s entire theory and its inspiring effects on the scientific world, we would have to dedicate several articles to this topic.  Therefore, the main focus of this article is to explore the role of this important psychological theory on algorithms and the AI age.

In this case, the essence of Skinner‘s theory can be summarized as the impact of behavioral consequences on the shaping and continuing behavior or responses. 

This simple principle, which is the most important result of Skinner’s experiments and the essence of his theory of operant conditioning, has alone inspired fundamental developments in areas such as programmed learning and teaching machines, distance education, behavior modification, psychotherapy or behavior therapy, medicine and neurofeedback, principles of child-rearing, and currently artificial intelligence and machine learning. 

However, as usual, it should be noted that this important psychological theory needs to be better understood, and after recognizing its flaws and criticisms, its benefits and principles should be taken into account more in building the world of artificial intelligence and applying behavioral principles in designing artificial intelligence tools. 

 Therefore, by considering what critics of Skinner’s theory say, that it is too mechanical and radical and downplays the role of cognitive factors and human existence, we can take advantage of its benefits and key points, such as the crucial effect of consequences on behavior and response, as an essential key to designing better technology and taking steps towards a” Happy Modernity.”

Similarities of the Response Consequence Effect in Skinner’s Theory and AI Algorithms

Please consider the following points if you want a simple yet practical comparison. Then, you’ll know that understanding this comparison can help us better lead advanced artificial intelligence machines, regardless of the criticisms against Skinnerian behaviorism.

 Indeed, as one of the most influential contemporary psychologists, Skinner’s dream was precisely this: to create a disciplined behavioral technology and engineering that would enhance life and make it easier! 

Please consider these fundamentals:” Reinforcement” (both positive and negative) influences the repetition and likelihood of responses in organisms. “Positive reinforcement” increases the probability of behavior by its presence, while “negative reinforcement” increases the likelihood of response by its removal. However, the goal remains clear: the “consequence “influences behavior!

• Both in Skinnerian theory and in artificial intelligence algorithms, positive reinforcement is the same as reward, and negative reinforcement includes punishment and penalty.
• Another common aspect between Skinner’s operant conditioning and artificial intelligence is learning through interaction with the environment!  Most organisms learn through interaction and by gaining experience in the surrounding world.
• In operant conditioning and artificial intelligence, a relatively straightforward cycle is repeated: action, observation, and feedback.

This cycle is repeated until the desired outcomes are achieved! In addition to the points mentioned, operant conditioning has been directly incorporated into the design of reinforcement learning algorithms. Techniques such as Q-learning are model-free, value-based, off-policy algorithms that find the best series of actions based on the agent’s current state.

The term “Q” stands for quality, representing how valuable the action is in maximizing future rewards. The applications of this symbiosis between operant conditioning and reinforcement learning are extensive and diverse.

I have some suggestions for the useful Application of Skinner‘s Theory in Artificial Intelligence Technology.

Here, I have briefly listed more applications of operant conditioning theory in artificial intelligence technologies. Furthermore, I am very eager to hear your ideas and suggestions after reading these insights and my ideas.

Applications of Operant Conditioning in Artificial Intelligence: Bridging Behaviorism and Technology

From what was discussed in the previous section of this article, the applications of operant conditioning in artificial intelligence are almost evident.  However, if we want to define this synergy more specifically, my suggestions are as follows:

• In robotics, artificial intelligence tools can perform complex tasks through reinforcement learning, such as navigating unfamiliar environments or manipulating objects precisely.

• In the realm of autonomous vehicles, it appears that reinforcement learning mechanisms based on operant conditioning enable continuous adaptation to road conditions and traffic patterns. Thus, employing the simple principle of consequences on response leads to increased road safety and security by autonomous vehicles. 

• Besides robotics and autonomous intelligent systems, reinforcement learning has applications in various domains such as finance, healthcare, and gaming.

Notably, in designing principles of behavior therapy and therapeutic interventions, using the principle of response consequence and feedback is considered one of the influential principles in treating behavioral disorders.

Especially in medicine and clinical psychology, where discussing diagnosis and treatment through artificial intelligence is very hot, applying behavior therapy based on operant conditioning is inevitable.

Applying these principles in neurofeedback is highly recommended and has been the subject of extensive research for years.  In the world of education and learning through artificial intelligence algorithms, one of the primary principles of artificial intelligence application in education is personalized and learner-based learning.

It is implicit that this key principle of individual learning based on personal speed and rapid feedback is rooted in the same core principle of Skinner’s theory, which is the individual learning system based on response consequences.

Artificial intelligence in schools and higher education in advanced and developed countries is rapidly developing, and its most important feature is personalized learning based on consequences.  These consequences or feedback are provided to students by their learning partner and mentor, which is artificial intelligence. 

Another application is RLHF, which means “Reinforcement Learning with Human Feedback.” It’s a new area where computers learn from regular signals and direct input from people. This mix helps AI systems improve at tasks like making recommendations or controlling robots. RLHF is exciting because it lets humans and machines work together, making AI systems smarter and easier to understand. See this link https://johnnosta.medium.com/insights-on-ai-understanding-rlhf-f4b79cfcbdc8

 In general, artificial intelligence promises a revolutionary breakthrough in various fields through reinforcement learning and behavior optimization, from education and optimization of financial strategies to personalization of psychological and medical treatments.

However, significant ethical considerations are also required in this remarkable historical leap. As artificial intelligence systems increasingly become capable of shaping human behavior and guiding individual and social life, autonomy, privacy, and accountability issues take center stage. 

Therefore, ensuring that ethical principles and human values guide the application of reinforcement learning in artificial intelligence is essential to protect against unintended consequences and harmful outcomes.

In conclusion, B.F. Skinner’s operant conditioning theory has significantly shaped the landscape of artificial intelligence algorithms, particularly in reinforcement learning.

 By grasping the essence of behavior modification and the profound impact of consequences on behavior, AI systems stand to benefit across diverse fields, from robotics to healthcare and education.

However, it’s imperative to remain cognizant of ethical considerations, ensuring that AI deployment aligns with human values and ethical principles to mitigate potential risks and amplify societal benefits.

I invite you to read my articles on applications of behavioral theories in AI algorithms, available on MedikaLife and LinkedIn, for a deeper dive into this fascinating intersection of psychology and technology and to get “Happy Modernity” in the AI era.

The post From Skinner’s Operant Conditioning to Artificial Intelligence’s Algorithms appeared first on Medika Life.

They provide practical life solutions, based on logic, research, and scientific inquiry. However, it’s crucial to understand that theoretical assumptions aren’t always reliable everywhere and at all times. Sometimes they need updating and even replacement with new theories, especially in this era dominated by technology, where the pace of change extends to theories as well. 

As a social science researcher, I believe that for happy and fulfilling modernity, we must reassess all theories, especially those in the social sciences, to enhance their effectiveness, even if it means daring to fundamentally change scientific assumptions. 

The concept of “Happy Modernity,” which I’ve been discussing, refers to a sense of satisfaction, peace, and relative success in individual, professional, and social life in a world where artificial intelligence aids and augments human capabilities. 

This satisfaction stems not only from technology assisting the human mind but also from the joy and sense of accomplishment it brings in solving life and professional challenges for all groups. Amidst this, one effective solution could be cognitive approaches aiding in the construction of artificial intelligence algorithms.

This approach helps us understand how we can assist the mind and create conditions for a peaceful life for all humanity. One well-known theory is the cognitive approach associated with Piaget, about which I’ve written briefly, and at the end, I’ve discussed some of its applications. 

Cognitive Psychological theories about how our minds develop definitely influence the design and production of learning machines and artificial intelligence. 

 The Cognitive Development Theory of Jean Piaget as Model for AI

Piaget’s theory of cognitive development focuses on the stages that describe how children engage with the world, rather than just what they learn. Piaget studied children by engaging them in games, questions, and creating tests to understand their thought processes. Among cognitive psychology approaches, Jean Piaget’s name stands out, with many insights to offer. Piaget was a biologist whose theory influenced education, upbringing, and psychology more than any other field.

 Developmental psychology covers gradual changes from birth to adulthood.

Piaget focused on understanding how children think and acquire knowledge. Through observing the learning processes of his own children and others, Piaget developed his Theory of Cognitive Development in 1936. 

 This theory outlines Piaget’s Four Stages of Cognitive Development, which span from birth through adolescence. In summary, Piaget believed cognitive development passes through four stages:

(1) sensorimotor intelligence, (2) preoperational thinking, (3) concrete operational thinking, and (4) formal operational thinking.

 Each stage is correlated with an age period of childhood, but only approximately.

 How can Piaget’s Cognitive Development Stages Shapes AI Design?

 When we talk about stages, it means that the order must be followed, and until the first stage is completed, the second stage won’t begin, and so on. The timing of the beginning and end of each of these stages is approximate and depends on the brain’s internal calendar and nervous system.

 For example, until primary neural structures reach an acceptable level of growth, the stage of symbol development won’t happen. So, the timeline of cognitive and subsequent behavioral events is based on brain growth and maturation.

 Another point is that this order and occurrence are inclusive. This means that this order and flow happen in all human societies. The only exception is for children and individuals with intellectual disabilities and gifted children who are outside the normal curve; this flow is different and may have a faster or slower pace. 

 Additionally, environmental mental stimulation also affects the quality of these changes, although the foundation of these changes is basically a genetic and biological process and relies on stable internal systems. 

 From Criticism to Redefinition of Piaget to Interactive AI tools 

 It’s worth noting that this theory, like others, has received both praise and criticism. – One criticism is Piaget’s insistence on universal stages of development, which some critics argue does not account for significant environmental influences on the pace of growth and even intellectual performance! 

What aspects of Piaget’s theory can we use to embrace a happy and successful modernity? 

To answer this, we must revisit some key concepts in his theory and explore how they function in today’s world. Moreover, the important question is, how does Piaget’s theory get updated in the era of artificial intelligence? 

Answering this question requires new researches which cannot include in the short article, but I’ll try to briefly express my perspectives. Perhaps in the next article, I’ll delve deeper into it.

 Here are some my assumptions: 

 – Piaget considered a chronological framework for stages, limiting the influence of environment and environmental conditions in favor of genetics and cognitive readiness. 

Since the introduction of this perspective, this limitation has been criticized. In the era of artificial intelligence, where machines and algorithms are expected to assist the human mind, the focus is on maximizing the mind’s capacity, and mental limitations become meaningless. It’s worth noting that Piaget’s perspective is valuable for understanding children’s developmental stages and is still applicable in constructing artificial intelligence algorithms.

However, given the rapid advancements in human’s intelligent assistants(AI), limitations, including age limitations, become less significant! 

 – Another point in applying this theory in the modern era is the role of information, experience, and the interaction of these internal factors with available human information. 

Piaget believes that although genetics and brain development guide growth, humans, as active beings, constantly examine internal factors received through experience and sensory information with available information. As a result, they either absorb assimilate or adapt themselves to fit entirely new information, leading to growth. 

 – Taking this into account, we must ask, what is experience, and with which experiences is modern humanity interacting? 

 The answer is that the inputs and information that today’s humans receive from artificial intelligence tools are very different from Piaget’s intended inputs and actions! Modern humans have a powerful assistant that provides complex experiences and even readiness to the human mind in an instant, exposing them to thousands of brain storming at once, taking them beyond the realm of accessible experiences and information, and instead introducing them to a plethora of information.

 Consequently, access to quicker adaptation and high-level cognitive processes and performances such as creativity, innovation, problem-solving, critical thinking, etc., occurs in a fraction of a second. So, while interaction with external experiences and information still exists, this interaction is much faster than what Piaget describes. 

Furthermore, more and more adaptation occurs to the point where essential mental functions like creativity, innovation, and other cognitive actions are not distant events but seem much easier with a much more powerful assistant than before.

 Conclusion

Piaget’s key concepts are well-known and have been studied for years. Some of the most important include assimilation, accommodation, cognitive development stages, and equilibrium. In this short article, I focused on cognitive development stages and adaptation because they can guide the design and guidance of learning machines and AI tools. There is still a lot of research on Piaget’s developmental stages underway, but our discussion point is how usable and updatable this theory is in the current times and in the AI era to get Happy Modernity.

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