PROJECT TITLE: INCREASING DATA SCIENCE LITERACY & INNOVATION IN UBC UNDERGRADUATE MEDICAL EDUCATION
Student: Maya Koblanski, UBC MD 2027
Maya Koblanski’s MEDD 429 FLEX project focused on equipping UBC medical students with foundational data science and AI literacy through three integrated initiatives:
- Data Science & AI Literacy Module
In collaboration with the UBC Data Science and Health (DASH) Cluster, Maya led a student team in developing an introductory, interactive e-learning module (using Articulate360). The module covers core AI/data science concepts, critical appraisal of AI tools, ethical considerations, and the value of interdisciplinary collaboration. The module was built to inform medical students on foundational data science, critical appraisal of AI tools, ethical considerations, and the importance of interdisciplinary collaboration. It was designed to be concise and will be disseminated as a UBC Medicine module and on the DASH webpage. The project's approach aligns with research indicating that a national AI curriculum for undergraduate medical education should emphasize practical application, ethics, and interdisciplinary collaboration, suggesting that future doctors should understand and critically evaluate AI without necessarily needing to program it. Literature also suggests that data science, including machine learning and data ethics, should be a core skill for medical students.
- Randomized Controlled Trial (RCT)
In collaboration with Dheyaa Al-Najafi (UBC Medicine Student Learning Group (SLG) President), Maya co-designed and gained ethics approval for an RCT comparing AI-generated vs. human-generated medical school exam questions. Conducted using UBC RedCap, the study evaluated perceived preparedness and performance among first-year students. The trial later evolved to compare two AI models, aiming to assess question quality and efficiency, after an initial finding that using AI to create medical school exam questions is approximately 20 times more efficient than human-made questions. This aligns with systematic reviews suggesting that Large Language Models (LLMs) can produce adequate multiple-choice questions (MCQs) for medical exams, though human oversight and modifications are still required. The results were presented at FLEX Activity Day and may be published in medical education journals.
- UBC Medicine Datathon
Maya co-founded and led the first-ever UBC Medicine Datathon with Andy Hsu (UBC MD 2027, computer scientist and medical student), bringing together interdisciplinary participants (from medicine, data science, and engineering) to build machine learning models on chest x-ray and stroke prediction datasets. She managed program hosting, logistics, mentorship, and event promotion. Participants appreciated the importance of interdisciplinary collaboration and basic data science/AI literacy. The Datathon successfully engaged 70 participants in 10 interdisciplinary teams to build machine learning programs for early stroke detection and analyzing chest x-rays. This outcome is consistent with case studies demonstrating that datathons can effectively increase medical student AI literacy and confidence, encouraging the addition of such experiential learning activities in medical training. Highlights from the Datathon are available on the DASH event webpage.
This project highlights a crucial shift in medical education, acknowledging that while AI offers immense potential for efficiency and enhanced learning tools, it also necessitates a strong emphasis on ethical considerations, critical appraisal, and interdisciplinary collaboration. Like a compass for future navigators, these initiatives are guiding medical students to confidently steer through the evolving landscape of healthcare, where data science and AI are becoming indispensable tools for patient care and innovation.
Acknowledgements for contributions to Data Science & AI Literacy Module:
- Maya Koblanski, UBC MD 2027 (coordinator/ educational content developer)
- Dheyaa Al-Najafi, UBC MD 2027 (educational content developer)
- Lucy Hui, UBC MD 2027 (educational content developer)
- Talyna Szymanski, UBC Okanagan, Health and Exercise Sciences with a concentration in Kinesiology and Allied Health, and pursuing a certification or minor in Communications and Rhetoric Studies (educational content developer)
- Katherine Feng, UBC MD 2028 (educational content developer)
- Steven Chen, UBC MD 2029 (educational content developer)
- Dr. Ricky Hu, Internal Medicine Resident, UBC (editor)
- Dr. Rohit Singla, MD/PhD Student, UBC (editor)
- Stefanie Cheah, Senior Research Manager, Department of Medicine, UBC (editor)
- Varleen Kaur, Research Coordinator, Department of Medicine, UBC (editor)
- Dr. Anita Palepu, Head, Department of Medicine (project supervisor)