AI-driven Platform Aims to Improve Medical Device Maintenance in Low‑Income Settings

Global: AI-driven Platform Aims to Improve Medical Device Maintenance in Low‑Income Settings

An AI-powered support platform has been created to assist biomedical technicians in diagnosing and repairing medical devices in real time, targeting low- and middle-income countries where equipment downtime is common. The system combines a large language model with a web‑based interface, allowing users to submit error codes or symptom descriptions and receive step‑by‑step troubleshooting instructions. Researchers developed the concept to address gaps in timely maintenance, limited technical expertise, and insufficient manufacturer support for donated or third‑party devices.

Problem Context

In many resource‑constrained health systems, a substantial share of diagnostic equipment remains idle or non‑functional due to delayed maintenance and a shortage of skilled technicians. These challenges can prolong diagnostic delays and affect patient outcomes, especially when devices are obtained through donations that lack ongoing service agreements.

System Architecture

The platform integrates a large language model trained on biomedical device documentation with a user‑friendly web portal. Technicians input error codes or describe device behavior, and the model generates detailed repair guidance. Additionally, a global peer‑to‑peer discussion forum is embedded to facilitate knowledge exchange for rare or undocumented issues, enabling community‑driven support.

Proof of Concept

Researchers implemented a prototype for the Philips HDI 5000 ultrasound machine. The prototype was evaluated by feeding known error codes and symptom descriptions into the system and comparing the generated recommendations with manufacturer guidelines.

Results

The evaluation reported 100% precision in interpreting error codes, meaning every code entered was correctly identified by the model. For suggested corrective actions, the system achieved 80% accuracy, indicating that four out of five recommendations matched the established repair procedures.

Implications

If adopted broadly, the platform could reduce equipment downtime by providing immediate, accurate troubleshooting assistance, potentially improving diagnostic capacity in underserved hospitals. Faster repairs may also lower reliance on external service contracts and extend the usable lifespan of donated equipment.

Next Steps

The authors propose expanding the knowledge base to include additional device models and incorporating feedback loops from field technicians to refine the model’s recommendations. Partnerships with manufacturers and health ministries are suggested to align the platform with existing maintenance workflows.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.