AI in Medicine at UBC

Exploring how artificial intelligence can responsibly support clinical care, medical education, and health research.

We are a student-led group working across medicine, engineering, and health systems to study how AI can be used thoughtfully in real clinical and educational settings.

Active since 2019 Publications 2020–2025 Interdisciplinary, student-led research team

Research, education, and community at the intersection of AI and health care

Research

We publish peer-reviewed work on AI in medical education, clinical workflows, and family medicine.

Projects

We design and evaluate practical initiatives, from student-led workshops to clinical documentation and workflow research.

Education

We build curriculum, workshops, and resources that improve AI literacy for medical and health professional learners.

Community

We connect students, clinicians, and researchers across disciplines to support responsible AI adoption in health care.

Selected publications and practice-facing articles

Our work spans peer-reviewed journals, practice publications, and curriculum research — grounded in real clinical and educational settings.

2020 Medical Education

Introductory machine learning for medical students: A pilot

A student-led pilot course at UBC introduced medical students to machine learning through a 10-hour extracurricular curriculum combining lectures, Python workshops, guest speakers, and a team project.

Key Insight: First introductory ML course for medical students in Canada. 84% of respondents had little to no coding experience; students rated the course 4.5/5 and achieved 92% accuracy classifying breast tumours.

2022 Communications Medicine

Insights from teaching artificial intelligence to medical students in Canada

Describes a five-week Introduction to Medical AI workshop delivered across three iterations and refined to fit how medical students learn, emphasizing case-based teaching and guided programming.

Key Insight: The program grew from 8 students in 2019 to 225 registrants from 8 Canadian medical schools in 2020, showing that student-led AI education can scale nationally.

2022 JMIR Medical Education

Health care students' perspectives on artificial intelligence: Countrywide survey in Canada

A national survey examining how students across health professions understand AI, how they expect it to affect their work, and what they want from future curricula.

Key Insight: 2,167 respondents across 10 health professions and 18 universities. 51% could not define AI accurately; 79% expected AI to affect their careers; 75% had a positive outlook.

2023 PLOS Digital Health

An evidence-based approach to artificial intelligence education for medical students: A systematic review

A systematic review synthesizing literature on what medical students should learn about AI and how AI education should be implemented responsibly.

Key Insight: Screened 991 records and included 34 studies, identifying six recurring curriculum themes: ethics, theory, communication, collaboration, quality improvement, and perception.

2024 npj Digital Medicine

Developing a Canadian artificial intelligence medical curriculum using a Delphi study

A national Delphi study translating prior survey and review work into a concrete competency-based curriculum framework for Canadian undergraduate medical education.

Key Insight: 18 experts across 3 survey rounds judged 82 of 107 curricular competencies essential — one of the clearest consensus-based curriculum frameworks in AI medical education.

2024 This Changed My Practice (UBC CPD)

Talking smart: conversational AI streamlines clinical practice

A practice-focused article on conversational AI and digital scribes in family medicine, centered on documentation burden, burnout, and cautious use of AI in patient encounters.

Key Insight: Physician burnout rates as high as 45.7% in Canada with an estimated annual cost of $213 million. Digital scribes are positioned as a response to documentation burden — with physician review, consent, and privacy safeguards required.

2025 BC Medical Journal

Artificial intelligence in family medicine: Opportunities, impacts, and challenges

Maps how AI may influence the continuum of patient care in family medicine — from patient engagement and encounter preparation to diagnosis, treatment planning, and long-term management.

Key Insight: Organizes AI use across five phases of care. Cites a gastroenterology study where AI-based record organization saved clinicians 18% of the time spent addressing clinical questions without reducing accuracy.

Publication and milestone history

2020

Medical Education pilot course publication

2022

Communications Medicine workshop paper

2022

JMIR national student survey — 2,167 respondents across 18 universities

2023

PLOS Digital Health systematic review — 34 studies, 6 curriculum themes

2024

npj Digital Medicine Delphi curriculum paper — 82 essential competencies

2024

Talking smart practice article — conversational AI in family medicine

2025

BC Medical Journal — AI across five phases of family medicine care

What our members have built, studied, and contributed to

Student-Led AI Curriculum Workshops

Designed and delivered introductory AI workshops for medical students, growing from a single UBC cohort to a nationally delivered program across Canadian medical schools.

Workshop · Curriculum

National Survey on AI Literacy

Surveyed 2,167 students across 10 health professions and 18 universities to assess AI knowledge, expectations, and curricular needs in Canadian health education.

Publication · Survey

AI Curriculum for Medical Education

Developed a consensus-based competency framework through a three-round Delphi study with 18 national experts, identifying 82 essential curricular competencies.

Curriculum · Publication

Digital Scribe Workflow Research

Studied conversational AI and digital scribes as tools for reducing documentation burden and physician burnout in family medicine, with emphasis on privacy and consent.

Article · Research