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08/01/26

Radiologically relevant clinical history summarization with large language models: a multireader performance study.

Serapio, Adrian; Chen, Timothy L; Tangsombatvisit, Brian; Fields, Brandon K K; Yu, Yannan; Guo, Yue; Kim, Soo Kyung; Miao, Brenda Y; Sushil, Madhumita; Hess, Christopher P; Majumdar, Sharmila; Sohn, Jae Ho. Radiology.

Collection Topics
AI and Diagnosis Health IT Tests, Screening, and Laboratory Processes
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Summary

The quality of clinical information reaching radiologists can influence diagnostic interpretation. This multireader study compared LLM-generated radiology-relevant histories with clinician-provided indications for 241 examinations. Claude 3.5 Sonnet and Qwen 2.5-7B Instruct produced more comprehensive and factual summaries. The study however only assesses information quality and usefulness—not whether LLMs improve diagnostic accuracy or patient outcomes.
Read the paper
Serapio A, Chen TL, Tangsombatvisit B, et al. Radiologically relevant clinical history summarization with large language models: a multireader performance study. Radiology. 2026;320(2):e253238.