在本地设备上实现乳腺癌多学科会诊的隐私保护智能辅助，提升效率与决策质量。
Development and Feasibility Evaluation of an Edge AI as Medical Device System for Breast Cancer Multidisciplinary Team Meetings
- 使用开源语音识别与大模型构建本地化AI流程，全程不上传患者数据。
- 语音识别错误率降低20.7%，生成建议符合指南比例是云端方案的2.3倍。
- 适合临床医生快速记录、推荐治疗和分诊，但需解决流程整合与信任问题。
乳腺癌多学科会诊（MDT）面临病例复杂、时间紧迫及文档要求高的挑战，现有基于云的AI系统因涉及患者敏感信息而受限。本文开发了一套全本地运行的AI流水线，采用开源自动语音识别（ASR）与大语言模型（LLM），在单台NVIDIA Jetson AGX Orin设备上完成会诊录音转写、临床信息结构化及基于检索增强生成（RAG）的治疗建议生成，依据英国国家卫生与临床优化研究所（NICE）指南。评估包含两场模拟会诊、十组临床验证的合成讨论及1,270段声学增强录音。对Whisper large-v3的优化使录音错误率分别降低20.7%和24.4%；在增强音频上达到0.58%词错误率（WER）和1.58%词信息丢失，接近商用临床ASR基准。MedGemma-RAG识别出的与会诊一致干预措施比专有云端方案多2.3倍（p=0.020），整体准确率无显著差异。利益相关者认为自动化记录、治疗建议支持和病例分诊是最可信的短期应用，但强调工作流集成、治理机制与医生信任是关键实施挑战。结果表明，该隐私保护的本地AI系统在MDT文档与指南驱动决策支持方面具备可行性，为前瞻性临床评估奠定基础。
原文摘要 · Abstract (English)
Breast Cancer Multidisciplinary Team (MDT) meetings manage increasingly complex cases under considerable time pressure, and documentation requirements can reduce clinical efficiency and decision quality. Existing AI based MDT workflows rely on cloud-based processing, limiting their use because patient discussions contain identifiable information. We developed a fully on-device AI pipeline using open-source Automatic Speech Recognition (ASR) and Large Language Models (LLMs) that transcribes breast cancer MDT discussions, structures clinical information, and generates treatment recommendations using retrieval-augmented generation (RAG) grounded in National Institute for Health and Care Excellence (NICE) guidance. The pipeline runs on a single NVIDIA Jetson AGX Orin, ensuring that patient audio, transcripts, and outputs remain within institutional infrastructure. Evaluation included two recorded simulated MDT discussions, ten clinically validated synthetic discussions, and 1,270 acoustically augmented recordings. Optimisation of Whisper large-v3 reduced word error rate by 20.7% and 24.4% on the recorded discussions and achieved performance within 0.58% WER and 1.58% word information lost of a commercial clinical ASR benchmark on augmented audio. MedGemma-RAG identified 2.3 times more MDT-concordant interventions than a proprietary cloud comparator (p = 0.020), with no significant difference in overall accuracy. Stakeholders identified automated documentation, treatment recommendation support, and case triage as the most credible near-term applications while highlighting workflow integration, governance, and clinician trust as key implementation challenges. These findings demonstrate the feasibility of privacy-preserving, fully on-device AI for MDT documentation and guideline-informed decision support, providing a foundation for prospective clinical evaluation.
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