小模型RadPhi-3提升放射科报告生成与分析效率
RadPhi-3: Small Language Models for Radiology
- 基于Phi-3-mini微调，专注放射科多任务处理
- 在RaLEs基准上达当前最优性能
- 适合临床辅助、报告自动化与病理标注场景
基于大语言模型的协作助手在日常工作中很有用。目前正广泛探索如何以可靠方式应用AI助手支持放射科工作流程。本文提出RadPhi-3，一个从3.8亿参数的Phi-3-mini-4k-instruct微调而来的小型语言模型，用于协助完成多种放射科任务。除了以往研究较多的胸部X光报告印象摘要生成外，我们还探索了新任务，如对比当前与历史报告的变化摘要生成、从报告中提取结构化段落、对报告进行病灶、导管、线路或设备等标签标注。指令微调过程中，模型学习自放射科医生常用可信知识源Radiopaedia.org。RadPhi-3不仅能可靠回答放射科相关问题，还可完成多项报告处理任务。在RaLEs放射科报告生成基准测试中达到当前最优（SOTA）表现。
原文摘要 · Abstract (English)
LLM based copilot assistants are useful in everyday tasks. There is a proliferation in the exploration of AI assistant use cases to support radiology workflows in a reliable manner. In this work, we present RadPhi-3, a Small Language Model instruction tuned from Phi-3-mini-4k-instruct with 3.8B parameters to assist with various tasks in radiology workflows. While impression summary generation has been the primary task which has been explored in prior works w.r.t radiology reports of Chest X-rays, we also explore other useful tasks like change summary generation comparing the current radiology report and its prior report, section extraction from radiology reports, tagging the reports with various pathologies and tubes, lines or devices present in them etc. In-addition, instruction tuning RadPhi-3 involved learning from a credible knowledge source used by radiologists, Radiopaedia.org. RadPhi-3 can be used both to give reliable answers for radiology related queries as well as perform useful tasks related to radiology reports. RadPhi-3 achieves SOTA results on the RaLEs radiology report generation benchmark.
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