用AI让医疗设备在缺人地区也能自修，准确率提升超60%。
From Manuals to Maintenance: Fine-Tuning MedGemma for Multi-Modal Imaging System Support in Low-Resource Settings
- 用医学大模型微调，理解设备报错并生成维修步骤。
- 在1万+高质量问答数据上训练，准确率提升60%以上。
- 适合资源匮乏地区的医院和基层技术人员使用。
影像设备停机是低收入和中等收入国家（LMICs）医疗交付的主要障碍，常因缺乏专业生物医学工程支持所致。我们提出一种多模态医疗设备维护问答框架，并展示了对医学基础模型的微调，用于解决专业技术故障问题。基于九个LMICs的跨国调查，我们收集了磁共振与超声系统的技术手册，构建了INGENZI_DatasetV1，包含10,294对高质量、过滤后的问答-上下文数据。采用基于QLoRA的参数高效微调方法，将MedGemma-4b-it模型适配为可解析系统错误日志并生成分步维修指令。相比基线模型，微调后系统在各项指标上均有显著提升：F1分数从0.22升至0.38，ROUGE-2从0.18升至0.41，BERTScore F1从0.86升至0.91。这些结果表明模型对新故障查询生成的响应更精确、流程更准确。本研究为资源受限环境中的AI辅助诊断与维护工具建立了可靠基础。
原文摘要 · Abstract (English)
Imaging device downtime is a major barrier to healthcare delivery in low- and middle-income countries (LMICs), often driven by limited access to specialized biomedical engineering support. We present a multi-modality medical equipment maintenance question-answering (QA) framework and demonstrate the fine-tuning of a medical foundation model for specialized technical troubleshooting tasks. Guided by a multi-country survey across nine LMICs, we curated technical manuals from MRI and ultrasound systems to generate the INGENZI_DatasetV1, containing 10,294 high-quality, filtered QA-context pairs. Using QLoRA-based parameter-efficient fine-tuning, we adapted the MedGemma-4b-it model to interpret system error logs and generate step-by-step equipment repair instructions. Compared to the baseline model, the fine-tuned system achieved substantial improvements across metrics, including F1 score (0.22 to 0.38), ROUGE-2 (0.18 to 0.41), and BERTScore F1 (0.86 to 0.91). These metric gains demonstrate that the model generates significantly more precise and procedurally accurate technical responses to new troubleshooting queries. This work establishes a reliable foundation for AI-assisted diagnostic and maintenance tools in resource-constrained settings.
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