A scanner going down without warning is never just a technical inconvenience. It delays diagnoses, throws off hospital schedules, and puts extra strain on already stretched clinical teams. As imaging and interventional systems grow more sophisticated, the real challenge isn't keeping the machines running — it's knowing, in advance, when they won't be.
This is where artificial intelligence is starting to change the equation.
Every scanner in the field is quietly generating a trail of information: service records, performance logs, usage patterns, clinician feedback. Taken individually, these are just routine data points. Taken together, across an entire installed base, they start to tell a story about how equipment actually behaves in real hospitals, under real workloads — not just in a lab.
Turning scattered data into something useful
The trouble has always been volume and fragmentation. A CT system might show a recurring image artefact here, a workflow bottleneck during an interventional procedure there — but when that information sits in separate systems, reviewed in isolation, the pattern connecting them is easy to miss.
AI is good at exactly this kind of problem. By pulling together data from many machines and many sites, it can surface trends that no single service report would ever reveal on its own — an emerging reliability issue, a subtle shift in performance, an opportunity to fix something before it becomes a complaint.
That capability is what makes predictive maintenance possible.
From fixing problems to seeing them coming
For most of the industry's history, reliability issues became visible only after the fact — after a failure, a service call, a string of complaints. By then, the damage is already done: an emergency repair, a cancelled scan list, a disrupted care pathway.
AI offers a genuine alternative. By studying service histories, sensor readings, and past field events, models can pick up on early, subtle warning signs — the beginning of CT tube degradation, say, or an anomaly forming in an MR cooling system — well before either becomes an actual breakdown.
That early warning changes what maintenance looks like. Instead of reacting to a failure, engineering teams can plan around one. For hospitals, that means fewer cancelled scans, better use of expensive assets, and lower emergency service costs. For patients, it means one less reason for their diagnosis or treatment to be delayed.
Deciding what matters most, faster
As these systems get more connected and more software-driven, the decisions around them get harder, not easier. A single imaging platform might have thousands of units installed across very different healthcare settings. When an issue turns up, someone has to figure out fast how widespread it is, how serious it might be clinically, and whether it needs immediate action or can wait for a scheduled upgrade.
This is another place AI earns its keep — helping engineering teams weigh risk, installed-base exposure, and clinical impact together, rather than case by case. A subsystem issue in an MR platform, for instance, might warrant very different responses depending on how often it occurs and how much it actually affects patient care. Data-driven insight helps teams put their effort where it will actually matter, instead of spreading it thin.
The payoff for hospitals is a more stable, more predictable equipment fleet. The payoff for patients is that the procedures they depend on keep running without interruption.
Equipment that gets better over time
The bigger opportunity, longer term, is technology that doesn't just get maintained — it learns.
As medical systems generate more and richer data about their own performance, that data can become a feedback loop connecting field experience, service engineers, and clinical users. Instead of waiting for the next hardware generation, engineering teams can act on what the data shows right now — refining software, smoothing out workflow friction, resolving the small usability issues that never quite rise to the level of a formal complaint.
That's the real shift AI is enabling: from maintaining equipment to continuously improving it.
The future of sustaining engineering isn't only about keeping machines running longer. It's about building healthcare technology that adapts as it's used — so hospitals run more efficiently, clinical teams are better supported, and patients can count on the equipment their care depends on actually being there when they need it.
The author is the global sustaining engineering leader at Philips Indian Subcontinent.
The opinions expressed in this article are those of the author and do not purport to reflect the opinions or views of THE WEEK.
