AIME: Conference Workshop on Educating Users and Developers of AI in Healthcare (June 26)
Overview
This workshop is hosted by T-CAIREM at the University of Toronto and the American Medical Association.
The integration of Artificial Intelligence (AI) into healthcare is rapidly transforming medical practice. Successful deployment of AI models into healthcare requires interdisciplinary collaborations between clinicians and data scientists. Yet, such collaboration is impeded by the lack of shared understanding and language across disciplines. Additionally, there remains significant debate on the level of technical expertise required for healthcare professionals to safely leverage AI tools. This workshop will explore the core technical AI competencies necessary for clinicians using AI, as well as the tenets of healthcare that data scientists need to be aware of when developing AI for healthcare and health research. Through interactive discussions, case studies, and hands-on activities, participants will evaluate different AI education models, discuss legal and ethical considerations, and propose strategies fostering AI literacy among healthcare professionals. The session will conclude with structured data collection to capture participants' insights, informing a follow-up commentary on the evolving role of interdisciplinary collaboration and AI literacy in healthcare. To ensure the findings of this workshop translate to the broader community, our output will be a commentary summarizing the thoughts discussed in the workshop titled: The AI Learning Curve: What Physicians Need to Know About AI, and What Developers Need to Know About Medicine.
Workshop Motivation
The increasing deployment of AI in healthcare raises critical questions about how clinicians should be trained to interact with AI-driven decision support tools. While some argue that physicians need only a conceptual understanding of AI, others believe deeper technical training is necessary to ensure safe and effective use. Similarly, for AI developers, the extent of clinical expertise required has yet to be explicitly stated; indeed, an understanding of the healthcare context is pertinent as various assumptions about clinical variables and workflows are embedded within model architectures. Providing guidelines around the types of knowledge needed by each stakeholder (hospital, clinicians, developers) is pertinent to enable interdisciplinary collaboration. Clear guidelines around the knowledge of each stakeholder are also a prerequisite to current debates around equity and liability with deployed AI models. This topic is particularly relevant to AIME attendees, as AI literacy among healthcare professionals – and health literacy among AI developers – are foundational factors in determining the success of AI integration into clinical practice.
This workshop aims to generate actionable insights for medical AI education frameworks and AI developer training by bringing together diverse perspectives from medical educators, clinicians, AI developers, and policymakers. We intend to summarize these findings into a commentary. The findings will help inform curriculum development and institutional policies, ensuring that medical professionals and AI developers are adequately prepared to develop and engage with responsible and effective AI systems.
This workshop will explore key considerations of the education and interdisciplinary collaboration needed for integrating AI into medical education and clinical practice. The session will focus on the following topics:
- Knowledge needed across interdisciplinary stakeholders: We will explore the appropriate level of technical expertise required for clinicians to effectively and safely use AI-driven tools in decision-making, and conversely the appropriate clinical knowledge needed for developers to understand the clinical context.
- AI Literacy in Medical Education: We will establish foundational AI competencies for healthcare professionals, ensuring they can differentiate between types of AI (generative vs. non-generative), critically evaluate AI tools, and understand their care implications.
- Effective Model Cards for Healthcare: We will explore how model cards can be tailored for greater clinical utility and shared understanding.
- Effective Clinician-Facing Dashboards for AI Models: We will explore how interdisciplinary teams can design model outputs that prioritize safe patient care.
- Stakeholder Perspectives in Medical AI Implementation: Examining the roles and viewpoints of developers, clinicians, patients, hospital administrators, policymakers, and industry leaders in integrating AI into healthcare settings.
- Ethical, Legal, and Operational Challenges of AI Deployment: Addressing key concerns such as bias, data privacy, liability, regulatory considerations, and the logistical barriers to AI adoption in clinical workflows.
Session Timing
This workshop will be part of the AIME 2025 Conference. It will be a half-day event on June 26, 2025, from 9am to 1pm.
GitHub page for workshop participants
Workshop Chairs
Gemma Postill, MD/PhD Candidate, University of Toronto
As an MD/PhD Candidate, Gemma Postill has expertise in AI applications in healthcare outcome prediction and clinical decision support. She also has expertise in medical education, having led multiple initiatives on AI literacy for healthcare professionals. Gemma is actively involved in medical education research to develop an AI competency framework, and separately a clinician scientist competency framework. Together, the research and education initiatives she leads help to bridge the gap between AI development and real-world clinical implementation.
Kimberly D. Lomis, MD, American Medical Association
Kimberly D. Lomis, MD is Vice President for Medical Education Innovations at the American Medical Association. In that capacity, she guides the AMA ChangeMedEd® Initiative, partnering with medical schools, GME and CPD programs to impact over 30,000 medical learners across the United States. Themes of collaborative work and advocacy efforts among the institutions engaged in the initiative include competency-based medical education, training in health systems science across the continuum, development of master adaptive learners, coaching for health professionals, promoting diversity of the physician workforce and inclusive environments, addressing learner & faculty wellbeing, advancing educational technology and AI in medical education, and change management. Dr. Lomis oversees AMA’s Precision Education portfolio of projects leveraging data and technology to personalized and enhance medical education across the continuum.
Christopher Khoury MSc, MBA, American Medical Association
Christopher Khoury is vice president of strategic insights at the American Medical Association. The unit comprises a diverse team of researchers, analysts, and business professionals focused on assessing and interacting with emerging elements across health care, technology, and policy sectors. He has worked in the healthcare sector for over 22 years and serves on multiple advisory boards across health technology, venture, and social services. Christopher received his MBA from The Ohio State University Fisher College of Business magna cum laude. He also holds BS/MS from the University of Illinois in Electrical and Computer Engineering and MS in biomedical engineering from the University of Wisconsin.
Laura Rosella, PhD, University of Toronto
A globally recognized epidemiologist, Dr. Rosella is the Chief Scientist and Stephen Family Research Chair in Community Health at IBH. She is a Professor at the Dalla Lana School of Public Health, University of Toronto; founder of the Population Health Analytics Lab; and a Canada Research Chair in Population Health Analytics. As a trailblazer in AI, she co-leads a CIFAR-funded network advancing responsible AI for diabetes prevention, as well as a CIHR-funded national platform (AI4PH) to build AI capacity for transformative change in population and public health.
Additional Workshop Committee Members
Rahul G. Krishnan, PhD
Professor of Computer Science, University of Toronto
Muhammad Mamdani, MD PhD
Vice President, Data Science and Advanced Analytics, Unity Health Toronto
Director, Temerty Center for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto
Abhishek Moturu
Computer Science PhD Candidate, University of Toronto
Student Education Co-Lead, Temerty Center for Artificial Intelligence Research and Education in Medicine, University of Toronto
Julie Midroni
MD Candidate, University of Toronto
Education Affiliate, Temerty Center for Artificial Intelligence Research and Education in Medicine, University of Toronto
Vinyas Harish, MD PhD
MD/PhD Candidate, University of Toronto
Research in artificial intelligence for emergency preparedness of health systems