AI acts as a digital tutor, enhancing the learning experience through personalized feedback and realistic clinical simulations to help shape the next generation of healthcare professionals.
Jasmine Ong
Dr Jasmine Ong from the Duke-NUS AI + Medical Sciences Initiative and Chief Clinical Pharmacist at Singapore General Hospital are co-first authors of the paper. “AI exists to empower clinical educators and preceptors, not replace them. AI allows educators and preceptors to focus on what matters most, fostering meaningful connections with learners. Acting as a digital tutor, AI enhances the learning experience through personalized feedback and realistic clinical simulations, helping to shape the next generation of healthcare professionals.”
Despite the potential of AI, its use in medical education currently faces challenges with a lack of qualified trainers and a lack of tested implementation strategies. Another major concern about LLM is its accuracy and reliability, with hallucinations and fabricated information still a problem.
LLMs exhibit biases, particularly related to gender and race. Such biases, especially when embedded in the medical literature, risk perpetuating systemic disparities over time. In addition, privacy issues are emerging with the risk of patient information being compromised. Dr. Ning Yilin, senior research fellow at the Duke-NUS Center for Quantitative Medicine and co-first author of the paper, said: “As AI becomes more deeply integrated into medical education and training, we need to address the ethical concerns it raises, including ensuring appropriate use, maintaining the integrity of learning, and preventing unintended harm. These challenges require clear guidance and comprehensive, responsible design.”
“AI is transforming medical education around the world. By working on a comprehensive, global strategy and partnering across sectors, we can responsibly deploy generative AI to create more interactive and accessible training, and translate its outcomes into better patient care,” added Liu Nan, director of the Duke University AI + Medical Sciences Initiative, associate professor in the Duke University Center for Quantitative Medicine and senior author of the paper.
The researchers also noted that sustainable AI implementation in medical education and training requires close collaboration across sectors. Healthcare organizations, medical schools, industry partners, and government agencies must work together to develop solutions that are responsible, scalable, and evidence-based. The researchers hope that this collaboration will lead to the development of a practical framework for implementing AI-integrated medical education and physician training. These partnerships are also key to establishing funding models and resource support.
sauce: Duke NUS Medical School
