Article

Claim-Level Transparency Analysis of LLM-Generated Diagnostic Reports: A Metabolic and Endocrine Biomarker Study

2026-05-06

Abstract excerpt

Large language models are increasingly deployed in clinical decision-support contexts, yet systematic evaluation of their factual reliability in generating patient-specific diagnostic reports remains sparse, particularly for laboratory interpretation tasks. This study presents a controlled transparency experiment in which four frontier LLMs — Claude Sonnet 4.6, Claude Opus 4.6, GPT-5.2, and Gemini 3.1 Pro — each g...

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Literature Corpus work: 25cec9f1-1bc7-5d4b-a970-072764d8a759
DOI: 10.64898/2026.05.03.721751

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