|Curricular objective||Delivery recommendations||Extracurricular objective||Delivery recommendations|
|Promote physicians to be data-savvy consumers|
Students should be able to critically evaluate AI claims and understand the connection between models and clinical realities.
|Actively engage students with hands-on workshops focused around:|
Recognizing appropriate potential applications of AI to health data
Understanding how to discern between different methods that can be applied to data (e.g. the distinction between prediction and causal inference approaches)
|Promote student interest groups in AI|
Interested students should be encouraged to connect and build networks around their shared AI focus.
|Extend the broadly-used format of the “student interest group” to AI, enabling students to organize and autonomously host initiatives such as:|
Seminar events with prominent AI in Medicine speakers
Hackathons and datathons in collaboration with computer science and engineering students
|Instill durable fundamental concepts about AI, while avoiding technical specifics|
It is more important for students to have a robust conceptual understanding of AI and the structure of clinical data science than to understand constantly changing technical specifics.
|Incorporate lecture and self-learning module content around:|
The basic pipeline of data acquisition, cleaning, analysis, and visualization
Issues with data stewardship and data quality assurance in healthcare.
Classes of machine learning approaches and common issues with design and integration of AI into clinical practice
|Facilitate connections between medical students and industry in the health-AI space|
As AI in medicine is not siloed to solely academia or industry, students should have the opportunity to be exposed to the AI ecosystem in their local and broader communities
|Leverage partnerships (either at the Faculty level or the student group level) to offer:|
Student site visits to start-ups to learn about entrepreneurship and the creation of health AI products and services
Student research opportunities with health AI companies, or public-private partnerships
|Introduce frameworks for approaching ethical considerations, both clinically and at a systems level|
Students should appreciate fairness, accountability, and transparency as core AI analogues to the traditional bioethics principles of beneficence, non-maleficence, autonomy, and justice15.
|Students should participate in interactive case-based workshops and seminars lead by AI and ethics experts focused on:|
The special considerations AI requires at clinical and system levels in a case-based format
How fairness, accountability, and transparency directly relate to core clinical values of beneficence, non-maleficence, autonomy, and justice which must permeate through all aspects of their care
|Provide longitudinal programs to give students hands-on experience with real-world AI projects|
Theoretical knowledge should be supplemented with practical, real-world experience through formalized programs.
|Longitudinal programs can include but are not limited to:|
“Computing for Medicine”, a validated 14-week course offered to preclinical students in UofT’s MD program to promote computer literacy, algorithmic thinking, and cross-domain collaboration9
Non-technical projects involving AI or data science, such as using design-thinking approaches to implement existing AI tools into clinical practice and workflows
|Promote computer science/data science as a dual-training path for MD/PhD and MD/MSc students|
Students should be provided with partnered, formalized learning opportunities that provide training at the intersection of health and data science.
|Establish partnerships with institutes across computer science, biomedical engineering, the basic sciences, and public health, such as:|
U of T Faculty of Medicine’s partnerships with the Vector Institute for Artificial Intelligence, and Schwartz Reisman Institute for Technology and Society
Harvard Medical School’s Collaborative Health Sciences and Technology MD/PhD Offerings with MIT
|Encourage cross-disciplinary collaborations between medical students and data scientists|
Students should build interdisciplinary networks, and be encouraged to connect and collaborate with peers across faculties
|Take active steps to break past disciplinary silos through initiatives such as:|
Shared “AI in Medicine” journal clubs open to both medical students and computer science/engineering students
Collaborative events such as “datathons”, wherein ad-hoc interdisciplinary teams compete to answer clinical questions on open database