Stethoscope to Algorithm: Equipping Tomorrow’s Doctors for Artificial Intelligence Driven Healthcare
Bibliographic record
Abstract
Artificial Intelligence (AI) is transforming the delivery of patient-centred healthcare in Canada and around the globe. As the next generation of healthcare providers completes their medical education, it is critical to equip them with both digital literacy and the skills to effectively integrate AI into patient-centered care. In Canada, medical education is guided by the CanMEDS framework, which has recently transitioned to a competency-based medical education (CBME) model. CBME emphasizes outcomes-based learning, focusing on patient-centered care through direct observation and assessment of Entrustable Professional Activities (EPAs). These EPAs are specific, observable, and measurable units of professional practice, underpinned by milestones that track progression and facilitate continuous feedback to learners. The CBME framework is divided into four stages—transition to discipline, foundation, core, and transition to practice—and is structured around seven CanMEDS roles: Medical Expert, Communicator, Collaborator, Leader, Health Advocate, Scholar, and Professional. Despite the growing influence of AI in healthcare, there is a notable absence of AI-specific competencies for critically evaluating AI tools, interpreting AI-generated outputs, and safely and ethically integrating AI into clinical decision-making. To address these gaps, we propose the integration of AI-specific competencies into the CanMEDS framework. This integration should adopt a constructivist approach, leveraging active learning, case-based scenarios, simulations, and real-world experiences to prepare learners for the complexities of AI in clinical practice. These AI-specific competencies can be adapted for undergraduate medical education and tailored to align with the Royal College’s subspecialty groups, including imaging-based, internal medicine, surgery, pediatrics, critical care, obstetrics and gynecology, psychiatry, and other specialized areas. Central to this approach is the incorporation of feedback loops from both learners and instructors to ensure a sustained focus on patient-centered care. While concerns about cognitive load exist with the introduction of AI-specific competencies, AI’s generative capabilities can be harnessed for self-assessment and reflective practice, potentially mitigating this challenge. Through an exploration of global efforts to integrate AI into medical education, we identified gaps within the current CanMEDS framework and evaluated existing EPAs for Royal College subspecialties using Generative AI. Our findings highlight opportunities to embed AI competencies across training stages and milestones. Preliminary results suggest that the optimal strategy for integrating AI into the CanMEDS framework focuses on the core stage of resident training and the role of the Medical Expert. Rather than creating a new role centered on digital literacy and AI, we recommend augmenting the existing CanMEDS framework to incorporate these competencies. By leveraging the flexibility of the CanMEDS framework, we aim to establish AI-specific competencies that are measurable, progressive, and conducive to longitudinal learning and continuous feedback. This integration will prepare the next generation of healthcare providers to use AI safely and effectively in their practice while maintaining a patient-centered focus.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".