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May 13, 2026Medical Teacher2 citations

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

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KMKen MastersSVSofia ValanciJBJennifer Benjamin

Key Points

  • This research addresses data privacy concerns in Health Professions Education using locally-hosted language models.
  • Implemented locally-hosted Small Language Models for tasks in Health Professions Education
  • Minimized risk of data exposure for students, patients, and research subjects.
  • Provided technical details for effective implementation in educational settings.
  • Enhanced data privacy for patient data and research participant information.
  • Successfully integrated language models in teaching and research contexts.
  • Identified limitations and areas for further research on technology applicability.

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.

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Cite This Study

Masters et al. (2026) studied this question.

synapsesocial.com/papers/6a03cc1b1c527af8f1ecff8fhttps://doi.org/10.1080/0142159x.2026.2667293
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