Pivoting during a Pandemic: Reimagining Intern Orientation EPA-Based OSCE

Medical education and physician training in the era of artificial intelligence

ABSTRACT

With the rise of generative artificial intelligence (AI) and AI-powered chatbots, the landscape of medicine and healthcare is on the brink of significant transformation. This perspective delves into the prospective influence of AI on medical education, residency training and the continuing education of attending physicians or consultants. We begin by highlighting the constraints of the current education model, challenges in limited faculty, uniformity amidst burgeoning medical knowledge and the limitations in 'traditional' linear knowledge acquisition. We introduce 'AI-assisted' and 'AI-integrated' paradigms for medical education and physician training, targeting a more universal, accessible, high-quality and interconnected educational journey. We differentiate between essential knowledge for all physicians, specialised insights for clinician-scientists and mastery-level proficiency for clinician-computer scientists. With the transformative potential of AI in healthcare and service delivery, it is poised to reshape the pedagogy of medical education and residency training.

Harnessing the potential of large language models in medical education: promise and pitfalls

OBJECTIVES

To provide balanced consideration of the opportunities and challenges associated with integrating Large Language Models (LLMs) throughout the medical school continuum.

PROCESS

Narrative review of published literature contextualized by current reports of LLM application in medical education.

CONCLUSIONS

LLMs like OpenAI's ChatGPT can potentially revolutionize traditional teaching methodologies. LLMs offer several potential advantages to students, including direct access to vast information, facilitation of personalized learning experiences, and enhancement of clinical skills development. For faculty and instructors, LLMs can facilitate innovative approaches to teaching complex medical concepts and fostering student engagement. Notable challenges of LLMs integration include the risk of fostering academic misconduct, inadvertent overreliance on AI, potential dilution of critical thinking skills, concerns regarding the accuracy and reliability of LLM-generated content, and the possible implications on teaching staff.