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Reliability of Large Language Model Generated Clinical Reasoning in Assisted Reproductive Technology: Blinded

Dou Liu1,2,3, Ying Long1,3,4, Sophia Zuoqiu5

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Summary
This summary is machine-generated.

Generating reliable clinical chains-of-thought (CoTs) for AI in reproductive medicine requires strategic prompting. A selective few-shot approach, using diverse, high-quality examples, significantly improved CoT quality over other methods.

Keywords:
assisted reproductive technologychain-of-thoughtclinical data reliabilityexplainable artificial intelligencelarge language model

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Area of Science:

  • Medical Artificial Intelligence (AI)
  • Reproductive Medicine
  • Clinical Decision Support

Background:

  • High-quality clinical chains-of-thought (CoTs) are crucial for explainable medical AI.
  • Data scarcity currently limits the development of reliable CoTs.
  • The clinical reliability of large language model (LLM)-generated CoTs remains unclear.

Purpose of the Study:

  • To evaluate the clinical reliability of LLM-generated CoTs in reproductive medicine.
  • To identify effective prompting strategies for enhancing CoT quality.

Main Methods:

  • A blinded comparative study involving senior clinicians in assisted reproductive technology.
  • Evaluation of CoTs generated using zero-shot, random few-shot, and selective few-shot prompting strategies.
  • Comparison of expert clinician ratings with evaluations from a state-of-the-art AI model (GPT-4o).

Main Results:

  • The selective few-shot strategy significantly improved CoT logical clarity, information use, and clinical accuracy (P<.001).
  • Random few-shot prompting offered no significant improvement over the zero-shot baseline.
  • An AI evaluator (GPT-4o) failed to distinguish critical performance differences between strategies, highlighting the need for strategic prompt design.

Conclusions:

  • A "dual principles" framework combining "gold-standard depth" and "representative diversity" is proposed for generating trustworthy CoTs.
  • This research addresses the data bottleneck in reproductive medicine AI development.
  • Human expertise remains essential for evaluating the clinical reliability of AI-generated data.