Engineering AI-Driven Predictive Maintenance Systems for Medical Equipment

Abstract

The standard of patient care has gotten a lot better as medical technology has grown so quickly. But the dependability and life of medical tools are very important for keeping these progresses going. Unplanned downtime because of broken equipment can make it harder to care for patients and cost more to maintain. Scheduled checks and fixes done after the fact are common ways of maintaining things the old way, which might not catch problems before they break. This paper shows a new way to use AI-driven systems for predicted repair on hospital tools. Using machine learning algorithms and IoT (Internet of Things) devices, the suggested system checks on the health of equipment all the time, looks at data in real time, and guesses when problems might happen before they do. The AI model is taught by looking at old data from medical devices to find trends of wear and failure. This lets repair plans be made ahead of time. This forecast method increases the usage of equipment, lowers the cost of upkeep, and makes sure that medical gadgets are working at their best, which makes healthcare centres more efficient overall. In addition, the method makes medical operations safer by lowering the chance that tools will break down at crucial times. Case studies from healthcare settings show how well the system works in different kinds of medical technology, like surgery tools, testing machines, and life-support systems. The paper talks about the problems with execution, like combining data and making sure the model is correct, and gives ways to get around these problems. In the end, the suggested AI-driven predictive maintenance system would completely change how medical equipment is handled, leading to better care for patients, lower costs, and flawless operations.

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