Expert's Word: How can generative AI assist in medical training? - By Allan Pscheidt
With artificial intelligence and active methodologies, we train doctors who are more prepared, humane, and adaptable to the constant transformations in healthcare.
Hello everyone! ❤️
Today, we’ve invited an expert to discuss an essential topic in education: the personalization of teaching, with the support of generative artificial intelligence.
Dr. Allan Pscheidt's article takes us through a reflection on how technology, especially artificial intelligence, can transform the way doctors are trained.
In a scenario where knowledge evolves rapidly, how do we balance innovation and humanization? What are the challenges and benefits of this approach?
Let’s explore these questions with an expert's analysis on the subject.
Allan Pscheidt is an innovator and expert in education, science, and technology, holding a Ph.D. in Biodiversity and Environment and an MBA in Leadership and Innovation.
With over a decade of experience, he leads educational programs focused on active methodologies and creative learning.
Allan is the author of the book Artificial Intelligence in the Classroom: How Technology is Revolutionizing Education (in Portuguese). Additionally, he is a scientific communicator, speaker, and mentor, with a focus on topics such as AI in education, inclusion, diversity, and climate change.
How Can Generative AI-Based Technology Assist in Medical Training?
As an educator and researcher, in my years managing higher education programs in the health field, I have observed that medical training must consider the constant transformations in technology while still offering humanistic education pathways. Personalized learning in this context emerges as a way to accommodate different learning paces, study methods, and career trajectories—something essential when working with students from diverse backgrounds and experiences. Medical students should be able to explore their specific interests without losing focus on the core competencies of medicine, particularly regarding diagnosis and disease treatment.
Today's educational technologies increasingly allow for accessible learning monitoring and adaptation. Online platforms capable of providing immediate feedback, for example, enhance student autonomy. By tracking performance reports, individuals can identify areas for improvement and reinforcement. This approach not only facilitates the acquisition of clinical knowledge but also fosters motivation to advance in more complex and specialized topics.
Personalized learning in medical education means adjusting the content load and practical exercises as each student progresses. Instead of enforcing a standardized pace, as seen in traditional education, it is possible to design activities and assessments that consider each student’s preferences, challenges, and professional goals. This flexibility is particularly relevant in fields like medicine, which require mastery of both theoretical knowledge and practical skills.
This brings us to a topic that has gained significant attention in the past year: the public accessibility of various generative AI tools, such as ChatGPT, Gemini, Mistral, Claude, Perplexity, and more recently, DeepSeek. The large language models (LLMs) powering these generative AI tools are robust, extensively trained, and just a click away (or two clicks, if you count the subscription button first).
Integrating artificial intelligence with adaptive learning platforms in medical education has been shown to improve content retention and student engagement. Recent studies confirm that active methodologies, combined with AI-driven systems, can enhance the learning process and reduce comprehension gaps (Gilson et al., 2023; Eysenbach, 2023; Scherr et al., 2023, among others). In my view, implementing these solutions requires not only technological infrastructure but also proper training for educators.
The COVID-19 pandemic demonstrated that our reliance is primarily on data infrastructure rather than large classrooms or auditoriums. Even laboratories can be updated with virtual reality devices and digital synthetic models. In education, students now value a professor who is connected and up-to-date rather than one who simply has the greatest ability to memorize physiology or pathology atlases.
In medical school, objective assessments help verify competencies, but case studies and problem-solving exercises bring students closer to real-world professional practice. When aligned with personalized learning systems, these assessments and studies allow for the development of performance reports. Each student can better understand their challenges—whether in analyzing lab results or performing specific procedures. This targeted feedback serves as a learning compass, fostering individual responsibility and a focus on areas that require more attention.
Do you see how this approach frees up professors to focus on more complex procedures rather than constantly analyzing each student individually?
Challenges and Practical Aspects
Implementing a student-centered personalized learning approach comes with challenges. The first is a shift in mindset. Professors accustomed to traditional methods may question curriculum flexibility or personalized assessments. Institutional policies and regulatory frameworks set by the Ministry of Education can also pose obstacles.
Additionally, continuous faculty training, hardware and software updates, and a curriculum redesign focused on the real-world skills expected of medical professionals are necessary.
Infrastructure investments are also required. Adaptive learning systems need technical support and resources to function effectively. In large classes, ensuring equitable access can be difficult, as not all institutions have the financial means to support intensive technology use. However, when institutions are committed, these resources become powerful tools for making education more inclusive and effective. In the long run, it’s a win-win situation.
Benefits for Clinical Competencies
Personalized medical education enhances both clinical and communication skills. By integrating simulations and interactive scenarios tailored to each student’s level, ethical attitudes and empathy are reinforced while providing realistic professional experiences. Students can practice anamnesis, physical examinations, and decision-making in virtual environments, receiving immediate feedback. This allows them to improve their clinical reasoning in real-time, right when mistakes occur.
Practical experience in hospitals or clinics is often limited by time, patient availability, and case variety. While this may not be an issue in major urban centers like São Paulo’s metropolitan area, how can the same quality of education and opportunities be provided in remote regions? Virtual environments help overcome these limitations by simulating various medical situations that future doctors will encounter. When combined with generative AI, we can create images, videos, and interactive content, transforming traditionally text-based learning into more immersive experiences. By tracking individual progress, instructors can identify strengths and weaknesses, adjusting study plans as needed.
Data Analysis and Continuous Improvement
Data analysis tools offer valuable insights into students’ educational progress. By identifying correlations between study strategies and assessment performance, faculty and administrators can adjust curricula to optimize learning outcomes. This systematic evaluation process fosters continuous improvement and reinforces a commitment to high-quality education. Rather than relying solely on subjective impressions, medical training becomes evidence-based.
Generative AI models can analyze vast amounts of data in near real-time, providing feedback and suggesting adjustments that would take human faculty weeks or even months to identify. This makes the curriculum more dynamic and truly student-centered while also freeing educators from the burden of data analysis and other administrative tasks.
Conclusion
Personalized learning aligns well with the challenges of a rapidly evolving world, where knowledge is constantly updated due to globalization, climate change, emerging diseases, and pandemics. Adapting curricula and assessments to each student's pace and needs promotes inclusion, equity, and quality in medical education. When combined with active learning methodologies and expert faculty guidance, AI offers unique opportunities to develop better-prepared, more empathetic doctors who are attuned to human complexities.
Although implementing these changes is not simple, the results are rewarding: more confident, compassionate professionals who can face the daily challenges of medical practice. From an inclusive, evidence-based perspective, personalization emerges as a concrete path toward excellence in medical education.
References for Further Discussion:
Armitage R. C. (2023). ChatGPT: the threats to medical education. Postgraduate medical journal, 99(1176), 1130–1131. https://doi.org/10.1093/postmj/qgad046
Boscardin, C. K., Gin, B., Golde, P. B., & Hauer, K. E. (2024). ChatGPT and Generative Artificial Intelligence for Medical Education: Potential Impact and Opportunity. Academic medicine : journal of the Association of American Medical Colleges, 99(1), 22–27. https://doi.org/10.1097/ACM.0000000000005439
Eysenbach G. (2023). The Role of ChatGPT, Generative Language Models, and Artificial Intelligence in Medical Education: A Conversation With ChatGPT and a Call for Papers. JMIR medical education, 9, e46885. https://doi.org/10.2196/46885
Gilson, A., Safranek, C. W., Huang, T., Socrates, V., Chi, L., Taylor, R. A., & Chartash, D. (2023). How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge Assessment. JMIR medical education, 9, e45312. https://doi.org/10.2196/45312
Grabb D. (2023). ChatGPT in Medical Education: a Paradigm Shift or a Dangerous Tool?. Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry, 47(4), 439–440. https://doi.org/10.1007/s40596-023-01791-9
Lee H. (2024). The rise of ChatGPT: Exploring its potential in medical education. Anatomical sciences education, 17(5), 926–931. https://doi.org/10.1002/ase.2270
Mohammad, B., Supti, T., Alzubaidi, M., Shah, H., Alam, T., Shah, Z., & Househ, M. (2023). The Pros and Cons of Using ChatGPT in Medical Education: A Scoping Review. Studies in health technology and informatics, 305, 644–647. https://doi.org/10.3233/SHTI230580
Nguyen T. (2024). ChatGPT in Medical Education: A Precursor for Automation Bias?. JMIR medical education, 10, e50174. https://doi.org/10.2196/50174
Peacock, J., Austin, A., Shapiro, M., Battista, A., & Samuel, A. (2023). Accelerating medical education with ChatGPT: an implementation guide. MedEdPublish (2016), 13, 64. https://doi.org/10.12688/mep.19732.2
Preiksaitis, C., & Rose, C. (2023). Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review. JMIR medical education, 9, e48785. https://doi.org/10.2196/48785
Scherr, R., Halaseh, F. F., Spina, A., Andalib, S., & Rivera, R. (2023). ChatGPT Interactive Medical Simulations for Early Clinical Education: Case Study. JMIR medical education, 9, e49877. https://doi.org/10.2196/49877
Seetharaman R. (2023). Revolutionizing Medical Education: Can ChatGPT Boost Subjective Learning and Expression?. Journal of medical systems, 47(1), 61. https://doi.org/10.1007/s10916-023-01957-w
Tangadulrat, P., Sono, S., & Tangtrakulwanich, B. (2023). Using ChatGPT for Clinical Practice and Medical Education: Cross-Sectional Survey of Medical Students' and Physicians' Perceptions. JMIR medical education, 9, e50658. https://doi.org/10.2196/50658
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