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Improving O-RADS Risk Stratification from Ultrasound Reports: A Comparative Evaluation of Hybrid versus End-to-End LLM Reasoning Strategies

Research Medical/Healthcare AI

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TL;DR - A retrospective study found that a hybrid architecture—using an LLM to extract ultrasound features and deterministic rules to assign O-RADS categories—outperformed end-to-end LLM reasoning and original clinical reports. The approach matters because it improved accuracy while making guideline-based decisions more reliable and interpretable.

  • Eight LLMs and three reasoning strategies were evaluated on 390 ovarian masses from 310 patients.
  • Gemini 3.6 Flash with the hybrid strategy achieved 99.2% accuracy and a weighted kappa of 1.00 against expert consensus.
  • End-to-end strategies achieved 65.6%–95.9% accuracy, while original clinical reports achieved 87.7%.
  • Separating feature extraction from rule execution reduced classification errors and mitigated overstaging.

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Improving O-RADS Risk Stratification from Ultrasound Reports: A Comparative Evaluation of Hybrid versus End-to-End LLM Reasoning Strategies

arXiv cs.AI Xiaotong Tan, Chunli Qiu, Xin Liu, Qing Huang, Guangli Zhou, Bo Gao, Xiaoyan Song, Shuyan Wang, Xiuqin Wang, Wufeng Xue, Ruobing Huang, Dong Ni, Guowei Tao, Jun Cheng 2026-08-24 arXiv:2608.23061
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-22 14:32:07.778706 UTC

TL;DR - A retrospective study found that a hybrid architecture—using an LLM to extract ultrasound features and deterministic rules to assign O-RADS categories—outperformed end-to-end LLM reasoning and original clinical reports. The approach matters because it improved accuracy while making guideline-based decisions more reliable and interpretable.

  • Eight LLMs and three reasoning strategies were evaluated on 390 ovarian masses from 310 patients.
  • Gemini 3.6 Flash with the hybrid strategy achieved 99.2% accuracy and a weighted kappa of 1.00 against expert consensus.
  • End-to-end strategies achieved 65.6%–95.9% accuracy, while original clinical reports achieved 87.7%.
  • Separating feature extraction from rule execution reduced classification errors and mitigated overstaging.
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