AI助手帮基层医院修设备，让故障诊断更准更快。
Empowering Medical Equipment Sustainability in Low-Resource Settings: An AI-Powered Diagnostic and Support Platform for Biomedical Technicians
- 用大模型+网页界面，输入故障码就能获维修指导。
- 对错误代码理解准确率达100%，修复建议正确率80%。
- 适合缺乏专家的低资源医疗环境，也支持技术员交流经验。
在低收入和中等收入国家（LMICs），大量医疗诊断设备因缺乏及时维护、技术专家不足及制造商支持有限而闲置或无法使用，尤其在通过第三方供应商或捐赠获得的设备上更为严重。这一问题导致设备停机时间增加、诊断延迟，影响患者救治。本研究开发并验证了一个AI驱动的支持平台，帮助生物医学技术人员实时诊断和维修医疗设备。系统整合大型语言模型（LLM）与用户友好的网页界面，使影像技师/放射科技师与生物医学技术人员可输入错误代码或设备症状，获取精准的分步故障排查指引。平台还设有全球同行交流论坛，用于知识共享，应对罕见或未记录的问题。以飞利浦HDI 5000超声设备为原型进行概念验证，错误代码解析准确率达100%，修复建议准确率为80%。该研究证明了AI系统在支持医疗设备维护方面的可行性与潜力，旨在降低设备停机时间，改善资源匮乏环境下的医疗服务水平。
原文摘要 · Abstract (English)
In low- and middle-income countries (LMICs), a significant proportion of medical diagnostic equipment remains underutilized or non-functional due to a lack of timely maintenance, limited access to technical expertise, and minimal support from manufacturers, particularly for devices acquired through third-party vendors or donations. This challenge contributes to increased equipment downtime, delayed diagnoses, and compromised patient care. This research explores the development and validation of an AI-powered support platform designed to assist biomedical technicians in diagnosing and repairing medical devices in real-time. The system integrates a large language model (LLM) with a user-friendly web interface, enabling imaging technologists/radiographers and biomedical technicians to input error codes or device symptoms and receive accurate, step-by-step troubleshooting guidance. The platform also includes a global peer-to-peer discussion forum to support knowledge exchange and provide additional context for rare or undocumented issues. A proof of concept was developed using the Philips HDI 5000 ultrasound machine, achieving 100% precision in error code interpretation and 80% accuracy in suggesting corrective actions. This study demonstrates the feasibility and potential of AI-driven systems to support medical device maintenance, with the aim of reducing equipment downtime to improve healthcare delivery in resource-constrained environments.
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