arXiv 2510.230082026-05-25cs.AI

从提示优化到多维可信度评估：增强中文LLM生成的肝脏MRI报告的可信度——初步扩展至肺癌

From Prompt Optimization to Multi-Dimensional Credibility Evaluation: Enhancing Trustworthiness of Chinese LLM-Generated Liver MRI Reports -- with Preliminary Extension to Lung Cancer

Yu-Yue Pathology Research Center, Jinfeng Laboratory, Chongqing, China（渝粤病理研究所，金风实验室，重庆，中国）；T Magnetic Resonance Imaging Translational Medical Center, Department of Radiology, Southwest Hospital, Army Medical University, Chongqing, China（7T磁共振成像转化医学中心，放射科，西南医院，中国人民解放军军医大学，重庆，中国）

Qiuli Wang, Xinhuang Sun, Yonglin Chen, Jie Cheng, Yongxu Liu, Xingpeng Zhang, Xiaoming Li, Wei Chen

备注：: 10 pages, 6 figures, 4 tables

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

本研究提出多维可信度评估（MDCA）框架，并指导机构特定提示优化，以增强LLM生成的肝脏MRI报告的可信度，初步扩展至肺癌。

摘要：

大型语言模型（LLMs）在从影像学发现生成诊断结论方面展现出有前景的性能，从而支持放射学报告、实习生教育和质量控制。然而，关于如何在不同临床背景下优化提示设计的系统指导仍未被充分探索。此外，评估LLM生成的放射学报告可信度的全面且标准化的框架尚未建立。本研究旨在通过引入多维可信度评估（MDCA）框架并提供机构特定提示优化的指导，增强LLM生成的肝脏MRI报告的可信度。所提出的框架被应用于评估和比较几个先进LLM的性能，包括Kimi-K2-Instruct-0905、Qwen3-235B-A22B-Instruct-2507、DeepSeek-V3和ByteDance-Seed-OSS-36B-Instruct，使用SiliconFlow平台。

英文摘要：

Large language models (LLMs) have demonstrated promising performance in generating diagnostic conclusions from imaging findings, thereby supporting radiology reporting, trainee education, and quality control. However, systematic guidance on how to optimize prompt design across different clinical contexts remains underexplored. Moreover, a comprehensive and standardized framework for assessing the trustworthiness of LLM-generated radiology reports is yet to be established. This study aims to enhance the trustworthiness of LLM-generated liver MRI reports by introducing a Multi-Dimensional Credibility Assessment (MDCA) framework and providing guidance on institution-specific prompt optimization. The proposed framework is applied to evaluate and compare the performance of several advanced LLMs, including Kimi-K2-Instruct-0905, Qwen3-235B-A22B-Instruct-2507, DeepSeek-V3, and ByteDance-Seed-OSS-36B-Instruct, using the SiliconFlow platform.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。