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

## TL;DR

This study evaluates how reliable large language models are at generating clinical reasoning in reproductive medicine and finds that high-quality examples significantly improve their performance.

## Contribution

The study introduces a 'dual principles' framework for generating trustworthy clinical reasoning using strategic prompt design.

## Key findings

- The selective few-shot strategy significantly outperformed other methods in logical clarity, key information use, and clinical accuracy.
- Low-quality examples in the random few-shot strategy were as ineffective as no examples at all.
- Human experts, not AI evaluators, were able to detect differences in the quality of generated clinical reasoning.

## Abstract

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.

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

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 evaluations from a state-of-the-art AI model (GPT-4o).

The selective few-shot strategy significantly outperformed other strategies across logical clarity, use of key information, and clinical accuracy (P<.001). Critically, the random few-shot strategy offered no significant improvement over the zero-shot baseline, demonstrating that low-quality examples are as ineffective as no examples. The success of the selective strategy is attributed to 2 preliminary frameworks: “gold-standard depth” and “representative diversity.” Notably, the AI evaluator failed to discern these critical performance differences. Thus, clinical reliability depends on strategic prompt design rather than simply adding examples.

We propose a “dual principles” preliminary framework for generating trustworthy CoTs at scale in assisted reproductive technology. This work is a preliminary step toward addressing the data bottleneck in reproductive medicine. It also underscores the essential role of human expertise in evaluating generated clinical data.