Transforming Medical Education through Artificial Intelligence

This Collection supports and amplifies

research related to SDG3, SDG4 and SDG9.

Artificial intelligence (AI) and the resulting paradigm shift towards digital medicine are transforming medical education. Medical trainees are increasingly using AI to support their learning, preparation for examinations, and development of clinical skills. Medical schools and residency/fellowship programs are incorporating AI teaching into their curricula. However, evidence-based guidance for trainees, instructors, and program leadership with regards to effectively integrating AI into the current medical education paradigm remains limited. This guidance is necessary to ensure that AI is used and taught effectively, ethically, and equitably in medical education.

This collection invites submissions on topics related to the use and/or teaching of AI in undergraduate, postgraduate, and/or continuing medical education, including but not limited to:

1. Use of AI in medical education

       Applications across educational stages

• How can AI be used to support medical education at the undergraduate, postgraduate, and continuing education levels?
• What is the effectiveness of various AI tools for supporting different aspects of medical education, including didactic teaching, case-based learning, preparation for examinations, and clinical skills?
• What role does AI play in the admissions process for medical trainees and in monitoring their progress over time?
• What is the impact of AI tools on students’ learning, memory, and comprehension?
• How can institutions implement human-in-the-loop models for AI-based clinical workflow education (e.g., decision support, documentation, etc.)?

       Assessment and evaluation

• What are the implications of AI (assisted) evaluation tools on assessment fairness, reliability, and validity for medical trainees?
• How can AI support the assessment and evaluation of medical trainees using multimodal data sources?
• In what ways will AI affect feedback mechanisms within the learning healthcare environment and medical curricular systems?
• What role can AI play in measuring the development of core competencies during medical training?

       Ethics and governance

• How can AI be used in medical education while preserving academic integrity, ensuring fairness, and protecting patient confidentiality?
• What are governance models for regulating AI’s role in medical education (e.g., hallucination monitoring, bias mitigation, etc.)?
• What are the benefits, drawbacks, and ethical considerations related to using AI in medical education?

​     2. Teaching AI in medical education

       Curriculum development

• How is AI being taught at the undergraduate, postgraduate, and continuing medical education levels?
• What are practical examples of the development, implementation, and delivery of effective AI curricula in medical education?
• How can medical schools collaborate with the technology and engineering sectors to co-develop AI curricula?

       Core competencies

• What are the key pieces of knowledge and skills that clinicians should have with regard to AI, and how can this be taught effectively to trainees?
• How can competency-based frameworks (e.g., Accreditation Council for Graduate Medical Education core competencies) incorporate AI-related knowledge/skills and digital professionalism?
• How can AI be integrated into medical education to enhance learning outcomes while preserving students’ core competencies in knowledge acquisition, comprehension, and clinical reasoning?