Artificial intelligence (AI) is revolutionizing medical education

This article was exclusively written for The European Sting by Loh Pei Yi and Wang Jia Dong James, two Year Four medical students from Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore. They are affiliated with the International Federation of Medical Students Associations (IFMSA), cordial partner of The Sting. The opinions expressed in this piece belong strictly to the writer and do not necessarily reflect IFMSA’s view on the topic, nor The European Sting’s one.

Artificial intelligence (AI) is revolutionizing medical education, offering innovative tools that enhance learning, diagnosis, and patient care. By integrating AI into medical education, future physicians are empowered with real-time data analysis, personalized learning experiences, and improved decision-making skills, ultimately leading to better healthcare outcomes and more efficient medical training. In this article, we discuss three focus areas where AI can be harnessed to nurture physicians of the future.

Firstly, AI has been integrated into medical examinations, including Observed Structured Clinical Encounters (OSCEs) and postgraduate surgical assessments, such as video-based evaluations (1). This development is promising, as it promotes greater fairness by mitigating biases stemming from examiner prejudice, preferences, and human errors like lapses in attention. AI-driven video analysis of OSCEs can provide more objective evaluations, offering detailed feedback on areas for improvement—an aspect often limited in traditional examiner reports due to time constraints. However, inherent biases within AI training datasets, particularly those influenced by examiner biases, could perpetuate existing inequalities. Therefore, careful selection and scrutiny of these training sets are essential to ensure fair assessments. 

Secondly, the proliferation and mainstream adoption of generative AI tools have significantly lowered the barriers to generating clinical cases to hone medical students’ clinical skills. AI-powered tools including SimConverse (2), Oscer, and Geeky Medics (3) have provided medical students with easy access to AI-generated cases that test their medical reasoning abilities, thus reducing the need for manual and potentially time-intensive clinical case creation. Some of these tools even interactively simulate clinical communications without needing real patients, by analyzing students’ voice recordings and facial expressions and generating patient responses accordingly. While this provides a safe space for medical students to learn essential competencies in a simulated environment, we should also avoid the pitfall of having AI-based tools completely replace the traditional experience of speaking with and examining actual patients, to preserve the centrally important doctor-patient rapport.

Finally, AI can potentially help from the standpoint of medical educators as well. Learning analytics and student profiling models have received substantial attention in the field of education technology (4). Machine learning approaches (5) have been used to better characterize students’ learning styles, strengths and weaknesses and better tailor educational material to meet their personalized needs. At present, it is interesting that such methodologies have not been widely adopted in medical education. This is a focus area that we believe would enhance the effectiveness of medical education, especially with challenges of teaching growing cohorts of future physicians.  

In conclusion, while AI holds immense potential to revolutionize medical education through enhanced examination formats and reports, realistic case stimulations and personalized learner profiling, it must be integrated thoughtfully. By balancing technological advancements with the irreplaceable human elements of medical practice, we can ensure that AI serves as a powerful tool to enrich education without compromising the core values of empathy, fairness, and clinical judgment. Thus, we advocate for local medical students, senior doctors and health governance bodies to draw up guidelines for AI use in medical education in a multidisciplinary manner.  

References 

1. https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2817840
2. https://www.simconverse.com/ 
3. https://geekymedics.com/generate-osce-stations-with-ai/ 
4. https://www.sciencedirect.com/science/article/pii/S2096248720300369
5. https://www.igi-global.com/article/student-profile-modeling-using-boosting-algorithms/284084

About the author

Loh Pei Yi and Wang Jia Dong James are Year Four medical students from Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore. Both are interested in the holistic application of Artificial Intelligence in medicine and have prior experiences in the Asian Medical Students Association (Singapore) and their school’s MedTech Interest Group.

Trending now: