Using locally-hosted Small Language Models (SLMs) to protect student, patient and research subject data in Health Professions Education
WHAT WAS THE EDUCATIONAL CHALLENGE?: Cloud-based Large Language Models (LLMs) are being increasingly used for Health Professions Education (HPE) teaching and research. A major concern is data privacy, resulting in a potential exposure of student, patient, and research participant data. WHAT WAS THE SOLUTION AND HOW WAS IT IMPLEMENTED?: Language Models to assist the teacher and researcher in completing tasks while minimising the risk of data exposure. WHAT WERE THE LESSONS LEARNED AND WHAT ARE THE NEXT STEPS?: The main ethical task is achieved, but there may be limitations. In addition, technical details are given to assist in the effective implementation of the solution. Further detailed research in a range of environments will demonstrate their practicability, especially as the technology improves. The implications are far broader than the focus on research and teaching covered in this article.
Authors
- Heather MacNeill
- Sofia Valanci (ORCID: https://orcid.org/0000-0002-0593-5353)
- Jennifer Benjamin (ORCID: https://orcid.org/0000-0001-6085-5973)
- Neil Mehta
- Ken Masters (ORCID: https://orcid.org/0000-0003-3425-5020)
Institutions
- Baylor College of Medicine (US)
- University of Toronto (CA)
- Cleveland Clinic Lerner College of Medicine (US)
- Metropolitan University (RS)
- Sultan Qaboos University (OM)
- Case Western Reserve University (US)
- Royal College of Physicians and Surgeons of Canada (CA)
Publication Details
- Journal
- Medical Teacher
- Published
- 2026-05-10
- DOI
- https://doi.org/10.1080/0142159x.2026.2667293
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00