Feasibility of a Specialized Large Language Model for Postgraduate Medical Examination Preparation: Single-Center Proof-Of-Concept Study.

ABSTRACT:

Background

Large language models (LLMs) are increasingly used in medical education for feedback and grading; yet their role in postgraduate examination preparation remains uncertain due to inconsistent grading, hallucinations, and user acceptance.

Objective

This study evaluates the Personalized Anesthesia Study Support (PASS), a specialized GPT-4 model developed to assist candidates preparing for Singapore's postgraduate specialist anesthesiology examination. We assessed user acceptance, grading interrater reliability, and hallucination detection rates to determine the feasibility of integrating specialized LLMs into high-stakes examination preparation.

Methods

PASS was built on OpenAI's GPT-4 and adapted with domain-specific prompts and references. Twenty-one senior

SUBMITTER: Leong YH 

PROVIDER: S-EPMC12712563 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature