Empowering Medical Equipment Sustainability in Low-Resource Settings: An AI-Powered Diagnostic and Support Platform for Biomedical Technicians

2026-01-23

AI Summary

This study addresses the critical issue of underutilized medical equipment in low-resource settings due to inadequate maintenance, which severely compromises diagnostic and therapeutic quality. To tackle this challenge, the authors propose the first AI-assisted maintenance platform tailored for low-resource environments, integrating a large language model (LLM), a web-based interface, and an error-code parsing algorithm. The system enables biomedical technicians to input device malfunction descriptions and receive real-time, step-by-step troubleshooting guidance, while also facilitating knowledge sharing and augmentation through an integrated global peer forum. Evaluated on the Philips HDI 5000 ultrasound system, the platform achieved 100% accuracy in error-code interpretation and 80% accuracy in corrective recommendations, significantly enhancing equipment availability. This work provides the first empirical validation of the feasibility and effectiveness of LLMs in medical device maintenance.

Technology Category

• Humans and AI: Interaction Techniques and Devices
• Machine Learning: Hardware-aware ML
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• medical equipment sustainability
• low-resource settings
• equipment downtime
• biomedical technicians
• diagnostic delays

Innovation

• AI-powered diagnostics
• large language model (LLM)
• medical equipment maintenance
• low-resource settings
• biomedical technician support