Stethoscope to Algorithm: Equipping Tomorrow’s Doctors for Artificial Intelligence Driven Healthcare
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,006 |
| Communication savante | 0,005 | 0,006 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,005 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».