Building an Interdisciplinary Centre for AI Education: Strategy and Practice

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Authors: Julie Midroni, Gemma Postill, Abhishek Moturu, Nihal Haque, Laura C. Rosella
Cited by: 0
References: 19

Abstract

Precision medicine seeks to tailor care such that patients receive individualized treatment based on their specific presentation, biology, and risk factors. Artificial intelligence, defined as the field of computational systems that perform tasks requiring human-like intelligence, such as prediction, pattern recognition, and decision-making, is revolutionizing precision medicine through enhanced diagnostic accuracy, optimized treatments, and improved health outcomes. However, the benefits of artificial intelligence can only be realized if clinicians understand the technology’s limitations and strengths, and if developers appreciate the clinical, systemic, and workflow contexts in which their tools were developed and will be introduced. Without this mutual understanding, risks include automation bias, exacerbations of existing healthcare inequities, breaches of patient privacy, and ethical dilemmas that may erode trust in healthcare systems. To address these challenges, the Temerty Centre for Artificial Intelligence Research and Education in Medicine at the University of Toronto has developed an interdisciplinary educational team and mandate. The mandate is organized into three guiding domains—principles, topics, and methods—that together provide a structured yet adaptable approach to artificial intelligence education. This commentary presents the rationale for this mandate and discusses its implications for precision medicine. Central to the mandate are commitments to formal, integrated training across disciplines, an emphasis on the ability to critically appraise artificial intelligence tools, and the delivery of interactive, evolving educational programs that are tailored to professional roles. As artificial intelligence continues to shape the landscape of healthcare, interdisciplinary and adaptive education will be critical to ensuring ethical artificial intelligence deployment and use, continued patient-centered care, and personalized medicine.

Fields of Study & Subjects

Artificial Intelligence in Healthcare and Education Machine Learning in Healthcare Explainable Artificial Intelligence (XAI)

Publication details

Journal / source
University of Toronto Medical Journal
Volume
103
Issue
1
Publication date
5 January 2026