The integration of artificial intelligence (AI) into medical school curricula is essential to ensure that future healthcare practitioners are well-versed in digital technology upon entering medical practice. For this, a collaborative effort involving medical students from both local and international institutes has proposed a standardised AI-centric medical curriculum.
This proposal, titled “Artificial Intelligence Education: An Evidence-Based Medicine Approach for Consumers, Translators, and Developers,” has been published in Cell Reports Medicine (https://www.cell.com/cell-
The suggested AI curriculum is structured around four key pillars: Technical concepts, validation, ethics, and appraisal. These pillars aim to cater to varying levels of familiarity and competency among students, categorized as consumers, translators, and developers.
Consumers, representing all AI users, would gain knowledge to effectively select suitable AI tools for patient diagnoses and care. Translators, advanced users with a deeper understanding of data structures and AI patterns, could apply different machine-learning tasks for optimal data representation and interpretation in clinical settings. Developers, possessing expertise in both clinical and computational realms, would focus on designing AI operational flows and creating novel healthcare applications using patient data.
At present, there is a notable disparity in the provision of AI education in medical schools, ranging from basic introductory sessions for consumers to intensive research projects for future translators and developers. The opportunity exists to establish a universal AI-centered medical education foundation, fostering connections between AI concepts and evidence-based medicine.
Differential learning for students with varying aptitudes and backgrounds can be facilitated through optional courses and modules, enhancing AI skills. However, the curriculum should predominantly address consumers, preparing them for the dynamic clinical environment.
Various teaching formats, including case-based learning, project work on real-life clinical problems, and peer-to-peer teaching, can be employed. Inter-professional collaboration can be encouraged by providing opportunities for students in medicine, allied health, computer science, and engineering to collaborate.
Faye Ng Yu Ci, a Year 5 student at NUS Yong Loo Lin School of Medicine and the lead author of the paper, stressed the importance of preparing medical students for the increasing prevalence of AI in healthcare. At the AI Health Summit 2023, Professor Daniel Ting reaffirmed the commitment to enhance AI and data literacy in healthcare workers, emphasising the need for the safe and responsible use of AI.
Professor Joseph Sung, Dean of Lee Kong Chian School of Medicine, highlighted the necessity for early exposure to AI technologies for medical and nursing students. NUS Medicine has already established a compulsory Minor in Biomedical Informatics, training students in data science, AI, and information technology to enhance patient outcomes and information flow across healthcare systems.
Professor Chong Yap Seng, Dean of NUS Medicine, emphasized the importance of preparing students for the digital landscape, and Professor Thomas Coffman, Dean of Duke-NUS, underscored the need for training in using AI while recognizing its limitations and downsides.
