ArticleFrontiers in digital health2026
Discrepancy between medical knowledge and clinical reasoning: evaluating large language models in prenatal diagnosis.
Article in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Purpose: Large language models (LLMs) have demonstrated impressive performance across various medical examinations; however, their competency in subspecialty clinical reasoning, particularly in prenatal diagnosis, remains poorly characterized. Materials and methods: A cross-sectional comparative study was conducted by using 125 questions from China's nationally standardized Prenatal Ultrasound Screening Qualification Examination. Three LLMs and two human physicians were evaluated. Questions were classified along three independent taxonomic axes: modality, disease system, and question format. Cochran's Results: Overall accuracy did not differ significantly among the five test-takers ( Conclusion: LLMs are well-positioned to serve as powerful knowledge augmentation tools, but are not yet ready to function as autonomous clinical reasoners in this complex, ethically consequential subspecialty.
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