Reliability of Large Language Model Generated Clinical Reasoning in Assisted Reproductive Technology: Blinded Comparative Evaluation Study.

ABSTRACT:

Background

High-quality clinical chains-of-thought (CoTs) are essential for explainable medical artificial intelligence (AI); yet, their development is limited by data scarcity. Large language models can generate medical CoTs, but their clinical reliability is unclear.

Objective

We evaluated the clinical reliability of large language model-generated CoTs in reproductive medicine and examined prompting strategies to improve their quality.

Methods

In a blinded comparative study at a clinical center, senior clinicians in assisted reproductive technology evaluated CoTs generated via 3 distinct strategies: zero-shot, random few-shot (using random shallow examples), and selective few-shot (using diverse, high-quality examples). Expert ratings were then compared with evaluat

SUBMITTER: Liu D 

PROVIDER: S-EPMC12828306 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature