Medical Curricula should be AI-focused – Proposal
Published: 08 Dec 2023
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Artificial intelligence should be woven into medical school curricula so that students are conversant with digital technology when they enter medical practice.
A team comprising medical students from local and overseas medical schools has proposed a standardised Artificial Intelligence (AI)-centric medical curriculum to be implemented and taught in medical schools globally.
Their suggestion was published in Cell Reports Medicine, titled Artificial Intelligence Education: An evidence-based medicine approach for consumers, translators and developers.
Such an AI-based curriculum will cater to the differing levels of familiarity and competency of the students, which the paper’s authors have classified as consumers, translators and developers.
The first group comprises all users of AI who require adequate knowledge to sift out suitable AI tools that can be used effectively for patients’ diagnoses and care in clinical settings. Translators are advanced users with more in-depth understanding of data structures and AI patterns, who are capable of applying different machine learning tasks allowing optimal representation and interpretation of available data to meet clinical needs.
Developers would then focus on designing and developing the overall AI operational flow, possessing the understanding and knowledge to apply new architectures, hence driving the creation of novel applications for healthcare using patient data. With their backgrounds in both the clinical and computational scenes, developers would be best placed to assist both consumers and translators to implement new applications of AI, while safeguarding patients’ welfare and interests in the face of emerging technologies.
Currently, there is significant heterogeneity in the provision of AI education in medical schools, ranging from none or basic introductory sessions suited for consumers, to intensive research projects for future translators and developers. Nonetheless, the curriculum should still cater largely to consumers to prepare them for an ever-changing clinical environment.
To achieve optimum AI learning outcomes, differential learning for students of variable aptitudes and backgrounds can be enabled through optional courses and modules broadening and deepening skills in AI.
A range of teaching formats can be employed, including case-based learning, project work using real-life clinical problems, and peer-to-peer teaching. Interprofessional collaboration can also be advocated for by creating opportunities for students in medicine, allied health, computer science, and engineering to work together.
Regardless of which pathway a student chooses, the opportunity exists to help the learner draw connections between AI concepts and evidence-based medicine to help develop good clinical reasoning alongside AI literacy.
Read the media release here.
