Reducing unplanned imaging equipment downtime with AI-driven predictive services

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Jean Michel Gard, Global Services Senior Product Manager & Maheshika Peiris, Content Manager Global Services

Unplanned imaging equipment downtime can have a significant impact on healthcare operations, potentially reducing imaging capacity, disrupting clinical workflows, or delaying patient access to diagnostic services. These possible impacts could potentially contribute to financial losses. ^1,2

Imagine a future where unplanned downtime becomes a rare event rather than an operational reality.

At AAMI Exchange 2026, GE HealthCare Services invited attendees to explore what that future could look like. Through an immersive CT tunnel experience and leadership discussions on the evolving role of AI and service intelligence, we explored what becomes possible when connected systems move beyond simply reporting problems to helping anticipate them.

This is not a vision of tomorrow. It is a reality that is already taking shape today, and here’s how.

Throughout history, every industrial revolution has fundamentally redefined how we work and operate. The first revolution brought steam power, mechanizing production; the second introduced electricity, enabling scale and efficiency; the third leveraged information technology, digitizing processes; and now, we are in the fourth revolution, driven by artificial intelligence.

AI is no longer just a concept. It is actively changing how industries operate. By recognizing patterns across vast datasets, AI helps enable faster, more informed decision-making and supports a shift toward more predictive, adaptive ways of working. Rather than reacting to disruptions, healthcare systems now have the opportunity to anticipate them. ³˒⁴˒⁶

Today, innovation goes beyond equipment itself. We have reached a point where data, clinical operations and service intelligence all come together to help us anticipate and prevent issues before they impact patient experience.^{i1 , i4}

This operational shift directly addresses a critical industry vulnerability – unplanned equipment downtime.

The AI value chain: connecting workflows to people

What makes AI compelling is its ability to recognize complex patterns in massive, continuous data streams, connecting dots that human operators might easily overlook. When these hidden patterns become visible, clinical operations can naturally shift toward more adaptive, data-driven ways of working. ^4,5,6

This intelligence can help transform daily operations across three distinct steps:

Fluid processes: Static, rigid schedules can disappear as workflows dynamically adapt to real-time equipment health data.
Effective collaboration: Departments no longer need to operate in silos; data transparency helps clinical and technical teams align their schedules more effectively.
Faster and informed decisions: Real-time visibility helps minimize guesswork, helping managers make high-stakes operational decisions with confidence.

Ultimately, this value chain can help bring people and technology closer together. It provides frontline healthcare teams with effective tools, clear visibility, and the support needed to help teams make informed decisions.^i1˒i4

The value of predictable performance

Predictable performance is not just a technical metric; it is an operational anchor that can help create measurable value across multiple layers of the healthcare organization. At the absolute center of this ecosystem is the patient, whose expectations often include timely exams. ^1,2

To deliver consistently on that expectation, other stakeholder tiers must align. For a radiology leader, it is about making sure the equipment is available and ready when the patient needs it. For technologists, it's about having control and consistency over their daily schedules. For in-house HTM and biomed teams, it is about visibility and efficiency: being able to identify issues early with clarity and address them before they lead to a sudden schedule collapse and patient backlog due to unexpected shutdown of a system. ^2 ,4˒ⁱ¹

At the leadership level, the broader focus is on maximizing utilization, improving productivity, and avoiding costly disruptions to patient experience. ^1, ²˒ⁱ¹

The architecture of predictive services

Predictive services represent part of the next frontier in healthcare technology maintenance. Instead of waiting for catastrophic equipment failures or adhering strictly to rigid, arbitrary calendar schedules, these services help leverage real-time data and analytics to proactively help keep systems running smoothly. ²˒⁴˒ⁱ¹

This evolution builds on earlier advancements in AI-driven predictive maintenance, including technologies like OnWatch Predict for MRI, which demonstrated how digital twin technology and predictive analytics could help reduce unplanned downtime, improve uptime, and support more continuous imaging operations. ^i2 , i5

To illustrate this transformation, Jean Michel Gard, Global Services Senior Product Manager at GE HealthCare, describes the evolution of service maintenance using a three-stage GPS navigation framework. The comparison highlights how maintenance strategies have progressed from reactive approaches to predictive intelligence:ⁱ⁴

The First Era (Reactive Navigation): Drivers relied entirely on paper maps. These tools were static and frequently outdated; if a road was closed, you discovered the bottleneck only after you arrived. This mirrors traditional reactive maintenance in healthcare, where equipment is serviced based on fixed calendars or after a critical failure has already occurred. ²˒ⁱ⁴

The Second Era (Proactive Alerts): The industry introduced basic GPS devices. While these systems successfully guided you to a destination, they lacked real-time traffic updates or dynamic rerouting capabilities. This represents remote monitoring and basic alert systems in healthcare, where alerts trigger only after thresholds are crossed, offering limited operational foresight. ²˒ⁱ⁴

The Third Era (Predictive Intelligence): Today’s intelligent GPS utilizes real-time traffic data, incident detection, and proactive rerouting to help you avoid problems before you ever encounter them. This is the essence of predictive services. As Gard explains, advanced analytics, AI, and digital twins continuously monitor equipment performance, isolating early signs of component degradation to schedule interventions before a failure can manifest. ⁴˒^{i4, i5}

How predictive monitoring differs from basic remote monitoring

Predictive monitoring differs from basic remote monitoring because it does not wait for a threshold to be crossed. Instead, it picks up early signals before a threshold is reached, helping give teams lead time, a clear sense of risk, and clear visibility into what might be developing. Predictive analytics then help interpret what those signals and patterns really mean over time, drawing on service histories, real-time performance data, and trends. ^{4 ,6, i1, i2}

Traditional maintenance models rely on predefined triggers such as replacing a filter every 90 days regardless of actual wear. Predictive services break this mold, helping to enable targeted, proactive corrective actions, such as replacing a tube before any clinical degradation is observed. ^{2, i1, i3}

By bringing these layers together, predictive services help create a connected operational model that translates early signals into coordinated action across people, workflows, and systems. The result can represent a profound shift from reactive firefighting to truly proactive service delivery. Instead of constantly managing crises, teams can operate with early visibility, efficient coordination, and control over how and when interventions happen. ²˒⁴˒ⁱ¹

Clinically, this ecosystem can help deliver fewer canceled appointments, more on-time starts, smoother daily operations, and confidence in system readiness. ¹˒²˒ⁱ¹

For in-house HTM and biomed teams, this impact can be immediate and measurable. Through tools like MyGEHealthCare , they gain a unified, real-time view across their entire imaging fleet, operating with the same level of data visibility as our own teams. For example, with OnWatch Predict , they receive direct, automated notifications of emerging issues, helping them act quickly, schedule repairs during low-utilization windows, and enhance the patient experience.^{i2, i6}

How predictive services can support more stable imaging workflows

With a faster, more proactive service model, the next question is clear: what does this mean for the day-to-day experience of imaging departments? The potential impact can be categorized into four core strategic benefits that can ripple across the clinical ecosystem.

More predictable schedules: By enabling planned interventions during low-utilization windows like evenings or weekends, departments can help maintain consistent scheduling blocks, eliminate last-minute disruptions, and optimize staff resource allocation. ²˒ⁱ¹
Fewer patient cancellations: Forecasting performance degradation weeks in advance gives teams the necessary lead time to trigger proactive interventions. This can help allow patients to receive their scans and treatments as scheduled, which can help drive up satisfaction scores and strip away the heavy administrative burden of emergency rescheduling and follow-up coordination. ²˒⁴˒ⁱ²
Confidence in system performance: When frontline clinical teams know their imaging fleet is continuously monitored. This can help them shift their cognitive energy to critical clinical decision-making. Helping to remove the anxiety of technical interruptions can be especially beneficial in high-stakes emergency and high-acuity settings. ⁴˒⁶˒ⁱ¹
Strategic collaboration with service teams: Predictive services can help fundamentally change the relationship with the OEM. Instead of interacting exclusively during a hardware crisis, teams can engage in regular, data-driven dialogues regarding fleet health, usage trends, and optimization strategies. This can help transform an adversarial vendor dynamic into a shared partnership. ^{i1, i6}

These operational benefits illustrate a larger shift taking place across imaging environments. Predictive service intelligence is helping to enable healthcare organizations to move beyond reactive maintenance models toward a more connected, proactive approach to imaging operations, one built around earlier visibility, greater coordination, and more predictable system performance. By combining AI, connected systems, and real-time operational insights, imaging departments can help reduce unexpected downtime, improve scheduling stability, and create a reliable experience for both care teams and patients. ²˒⁴˒⁶˒ⁱ¹

The value is not only operational; it can become measurable across utilization, workflow continuity, patient access, and service efficiency. The future of imaging services may not be defined solely by how quickly teams respond to downtime, but by how effectively they can predict and prevent it. ²˒⁴˒ⁱ¹

In our next article, GE HealthCare Predictive Services in Action, we explore how technologies like Tube Watch and OnWatch Predict are helping imaging teams identify early signs of equipment degradation, coordinate proactive interventions, and avoid disruptions before they impact the patient experience.

References

1. Glassbeam. Disruption Costs and Their Impact on Imaging Departments.
https://www.glassbeam.com/how-disruption-costs-impact-imaging-departments

2. Canadian Drug Agency (CADTH/CDA-AMC). Predictive Maintenance for Medical Imaging Equipment. Canadian Medical Imaging Inventory Service Report. 2022.
https://www.cda-amc.ca/sites/default/files/attachments/2022-08/predictive_maintenance_for_medical_imaging_equipment.pdf

3. Alotaibi KMN, et al. A Novel Framework for AI-Powered Predictive Maintenance in Medical Imaging Equipment: Reducing Downtime and Enhancing Patient Care. Power System Technology. 2025.
https://www.powertechjournal.com/index.php/journal/article/view/2805

4. Abdul Jamil AS, Khalil A, Yunus MM, Fofah JG. Empowering Predictive Maintenance of Medical Equipment Through AI-Driven Condition Monitoring. In: Biomedical Engineering. Springer Nature Singapore; 2024.

https://doi.org/10.1007/978-981-97-9294-8_4

5. Tang Y, Zhou Y, Wu T, Wang C, Li Z, Li K. AI-driven Predictive Maintenance for Medical Imaging Equipment: A Deep Learning Framework Based on the IoMT Data. Reliability Engineering & System Safety.

https://www.researchgate.net/publication/399130716_AI-driven_predictive_maintenance_for_medical_imaging_equipment_a_deep_learning_framework_based_on_the_IoMT_data

6. Assessment of IoT-Driven Predictive Maintenance Strategies for Computed Tomography Equipment Using Machine Learning. IEEE. 2024.
https://doi.org/10.1109/ACCESS.2024.3518516