Benefits of integrating AI include:
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Improved first-time fix rates: AI can guide technicians through complex diagnostics, increasing the likelihood of resolving issues on the first attempt.
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Optimized preventative maintenance: Predictive analytics enable more effective scheduling of maintenance activities, reducing the incidence of unexpected breakdowns.
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Resource allocation: AI helps in prioritizing service requests based on urgency and impact, ensuring optimal use of technical resources.
The integration of AI not only enhances operational efficiency but also contributes to better patient outcomes by ensuring the reliability of medical devices.
Build vs. buy: Strategic considerations for AI implementation
Organizations face a critical decision in implementing AI solutions: developing proprietary systems or adopting existing platforms.
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Building in-house: While this offers customization, it often involves significant investment, longer development timelines, and ongoing maintenance challenges.
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Adopting existing solutions: Leveraging established AI platforms can accelerate deployment, reduce costs, and provide access to proven technologies with continuous updates.
The report suggests that, for many organizations, adopting ready-made AI solutions may offer a more efficient path to enhancing service operations.
A call to action for medical device service leaders
The message from the 2025 Service Benchmark Report couldn’t be clearer: the way we service medical devices needs to change — and fast. The pressure is mounting. Equipment is more complex, patients can’t wait, and service teams are stretched thin.
Now’s the time to act. By shifting left and leaning into AI, service leaders can stop playing catch-up and start getting ahead — solving issues earlier, reducing downtime, and keeping critical equipment running when it matters most.
This isn’t just about operational gains — it’s about making sure providers can deliver the care patients count on. In today’s fast-moving healthcare environment, those who wait risk falling behind.
About the author: Assaf Melochna’s experience incorporates strong leadership skills built upon a strong technical foundation. He is an expert in service, and has business and technical expertise in enterprise software. Assaf started Aquant with his co-founder Shahar with the vision of helping service companies transform the way they deliver service. Prior to starting Aquant, Assaf spent 10 years at ClickSoftware where he served in various positions in solution consulting and product innovation. Assaf brings a unique approach from his experience as a Major in the intelligence forces (IDF) where he specialized in turning massive data to knowledge, and knowledge to actions.
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Steven Ford
Improving Medical Device Reliability with AI
May 19, 2025 03:21
There's always room to improve complex processes. However, I'm not sure that I see a revolution ahead here. The issue is data collection from which to base the insight that AI can provide, which usually requires a person with his or her hands on the device.
CT, MRI, and other devices made currently already have very sophisticated tests prescribed by the manufacturer and very specific module replacement procedures, as well as human technical backup for those situations that don't fit into a common path. AI will not replace an engineer with a spare RF Amplifier who knows what to do.
I'm not sure that the observation that many devices need a follow-up repair within three months of a PM being completed is a cause for concern.
The weakest link in equipment reliability and patient safety is clinics and decision makers who don't have the proper maintenance performed. Close behind that are regulatory/supervisory agents of various types who don't do their jobs. We have human problems. I'd like to see an AI detection and enforcement algorithm for that!
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