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

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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