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.

翻译：在低收入和中等收入国家（LMICs），由于缺乏及时维护、技术专业知识获取途径有限以及制造商支持不足（特别是对于通过第三方供应商或捐赠获取的设备），大量医疗诊断设备处于未充分利用或无法正常运行的状态。这一挑战导致设备停机时间增加、诊断延迟以及患者护理质量受损。本研究探索并验证了一个AI驱动的支持平台的开发，旨在实时协助生物医学技术人员诊断和修复医疗设备。该系统将大型语言模型（LLM）与用户友好的网络界面相结合，使影像技术人员/放射技师及生物医学技术人员能够输入错误代码或设备症状，并获取精确的逐步故障排除指导。该平台还包含一个全球性的点对点讨论论坛，以支持知识交流，并为罕见或无文档记录的问题提供额外背景信息。研究使用Philips HDI 5000超声设备开发了概念验证，在错误代码解读方面实现了100%的精确度，并在建议纠正措施方面达到了80%的准确率。本研究证明了AI驱动系统支持医疗设备维护的可行性与潜力，旨在减少设备停机时间，从而改善资源受限环境下的医疗服务提供。