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Automating Case-Based Learning in Obstetrics and Gynecology: Validation of a Locally Deployed Large Language Model
Xueyan Hu1,2, Xiaoju Luo1, Yuan Zhao1
1Department of Obstetrics and Gynecology, Wenzhou People's Hospital, Wenzhou, Zhejiang, People's Republic of China.
Journal of Multidisciplinary Healthcare
|July 19, 2026
Summary
Large language models (LLMs) can automate the creation of obstetrics and gynecology (OB/GYN) Case-Based Learning (CBL) databases, extracting objective data with high accuracy. Human oversight is crucial for subjective elements to ensure clinical risk mitigation.
Area of Science:
- Medical Education Technology
- Artificial Intelligence in Healthcare
- Obstetrics and Gynecology Training
Background:
- Standardized obstetrics and gynecology (OB/GYN) residency training heavily utilizes Case-Based Learning (CBL).
- Manual curation of high-quality teaching cases for CBL is a significant time burden for educators.
Purpose of the Study:
- To develop and evaluate a methodological framework for automating the extraction and construction of structured CBL databases.
- To utilize a locally deployed large language model (LLM) for this automated process.
Main Methods:
- A Qwen3-8B-Instruct LLM analyzed 3678 OB/GYN PubMed case abstracts.
- Extraction focused on primary diagnosis, teaching points, clinical pitfalls, and difficulty levels.
- A ground-truth dataset from 100 cases, validated by senior OB/GYN educators, assessed accuracy and AI hallucination rates; teaching utility was rated on a Likert scale with ICC analysis.
Main Results:
- The LLM framework successfully categorized 3678 OB/GYN cases, with Reproductive Endocrinology/Obstetrics and Andrology/Male Infertility as largest cohorts.
- High precision was achieved for objective data: 96.0% for primary diagnoses and 92.0% for teaching points, with a low 3.0% AI hallucination rate.
- Pedagogical evaluations showed high diagnostic accuracy (Mean=4.48/5.0, ICC=0.92), but lower reliability for subjective elements like clinical pitfalls (ICC=0.65) and difficulty levels (ICC=0.54).
Conclusions:
- LLMs provide an efficient and scalable solution for building large-scale CBL databases in OB/GYN.
- While adept at extracting objective clinical facts, LLMs lack the tacit knowledge of senior clinicians, necessitating human oversight for content validation and risk mitigation.
- Future research should focus on evaluating the direct impact of these AI-generated CBL databases on resident competency and learning outcomes.