Empowering Medical Equipment Sustainability in Low-Resource Settings: An AI-Powered Diagnostic and Support Platform for Biomedical Technicians
Bernes Lorier Atabonfack, Ahmed Tahiru Issah, Mohammed Hardi Abdul Baaki, Clemence Ingabire, Tolulope Olusuyi, Maruf Adewole, Udunna C. Anazodo, Timothy X Brown
TL;DR
The paper addresses the persistent downtime and underutilization of medical devices in LMICs by proposing INGENZI Tech, an offline-capable, multilingual AI platform that assists biomedical technicians with real-time diagnostic guidance, error-code interpretation, and a peer forum for knowledge sharing. Using a phased mixed-methods approach, the authors demonstrate a proof-of-concept on the Philips HDI 5000 ultrasound with a RAG architecture and segmented vector stores, achieving 100% precision on error-code retrieval and 80% accuracy on instructional guidance. The study outlines a clear roadmap for forum integration, API/IoT connectivity, model optimization, pilot deployment, and multi-device expansion, emphasizing offline operation and scalability. Significance lies in reducing equipment downtime, enhancing technician capability, and advancing equitable digital health infrastructure in LMICs, with potential applicability to broader imaging devices and OEM-agnostic workflows.
Abstract
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.