用于乳腺癌多学科团队会议的边缘人工智能医疗设备系统的开发与可行性评估
Development and Feasibility Evaluation of an Edge AI as Medical Device System for Breast Cancer Multidisciplinary Team Meetings
AI总结：
该研究开发了基于边缘AI的乳腺癌MDT会议系统，利用开源ASR、LLM和RAG实现设备上运行，经评估其ASR与RAG性能优异，证明了该系统的可行性。
AI中文摘要：
乳腺癌多学科团队（MDT）会议需在巨大时间压力下处理日益复杂的病例，而文档记录要求会降低临床效率和决策质量。现有基于人工智能的MDT工作流程依赖基于云的处理，由于患者讨论包含可识别信息，限制了其应用。我们开发了一种完全在设备上运行的人工智能流程，使用开源自动语音识别（ASR）和大语言模型（LLM）对乳腺癌MDT讨论进行转录，结构化临床信息，并使用基于英国国家卫生与临床优化研究所（NICE）指南的检索增强生成（RAG）生成治疗建议。该流程在单个NVIDIA Jetson AGX Orin上运行，确保患者音频、转录文本和输出数据保留在机构基础设施内。评估包括两段录制的模拟MDT讨论、10个经临床验证的合成讨论以及1270个声学增强录音。对Whisper large-v3的优化使录制讨论的词错误率分别降低了20.7%和24.4%，在增强音频上的性能与商业临床ASR基准的词错误率相差0.58%，丢失的词信息为1.58%。MedGemma-RAG识别出与MDT一致的干预措施是专有云对比系统的2.3倍（p=0.020），总体准确率无显著差异。利益相关者认为自动文档记录、治疗建议支持和病例分诊是最可信的近期应用，同时强调工作流程整合、治理和临床医生信任是关键实施挑战。这些发现证明了隐私保护、完全在设备上运行的人工智能用于MDT文档记录和指南知情决策支持的可行性，为前瞻性临床评估提供了基础。
英文摘要：
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.
