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.

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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
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article

Using locally-hosted Small Language Models (SLMs) to protect student, patient and research subject data in Health Professions Education

Heather MacNeill, Sofia Valanci, Jennifer Benjamin, Neil Mehta et al.
Medical Teacher
Artificial Intelligence in Healthcare and Education
article

Using locally-hosted Small Language Models (SLMs) to protect student, patient and research subject data in Health Professions Education

Heather MacNeill, Sofia Valanci, Jennifer Benjamin, Neil Mehta, Ken Masters
article en

Abstract

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.

Medical Teacher
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)
Quality Education
Openalex Percentile: Top 7%
Artificial Intelligence in Healthcare and Education
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Using locally-hosted Small Language Models (SLMs) to protect student, patient and research subject data in Health Professions Education — Heather MacNeill, Sofia Valanci, et al. · Medical Teacher (2026) | TGRS Research Map | TGRS