Unplanned imaging equipment downtime has long been a costly vulnerability for healthcare systems
Unplanned imaging equipment downtime can reduce imaging capacity, disrupt clinical workflows, and delay patient access to diagnostic services, potentially contributing to financial losses. At AAMI Exchange 2026, GE HealthCare Services presented an AI-driven approach to predictive maintenance that moves beyond reporting problems to anticipating them.
Jean Michel Gard, Global Services Senior Product Manager at GE HealthCare, described the shift using a three-stage GPS navigation framework. The first era relied on paper maps - static, outdated, and useless until you hit a closed road. That mirrors traditional reactive maintenance, where equipment is serviced on fixed calendars or after a failure has already occurred.
The second era introduced basic GPS devices: functional but lacking real-time traffic data or dynamic rerouting. This represents remote monitoring and basic alert systems in healthcare, where alerts trigger only after thresholds are crossed. The third era, predictive intelligence, uses real-time traffic data and proactive rerouting to help drivers avoid problems before encountering them. As Gard explained, advanced analytics, AI, and digital twins continuously monitor equipment performance, isolating early signs of component degradation to schedule interventions before a failure can manifest.
What separates predictive monitoring from basic alerts
Predictive monitoring does not wait for a threshold to be crossed. It picks up early signals before a threshold is reached, giving teams lead time, a clear sense of risk, and visibility into what might be developing. Predictive analytics then interpret what those signals and patterns mean over time, drawing on service histories, real-time performance data, and trends.
Traditional maintenance models rely on predefined triggers such as replacing a filter every 90 days regardless of actual wear. Predictive services break this mold, enabling targeted, proactive corrective actions - such as replacing a tube before any clinical degradation is observed. The result is a shift from reactive firefighting to coordinated action across people, workflows, and systems.
This approach builds on earlier AI-driven predictive maintenance technologies, including OnWatch Predict for MRI, which demonstrated how digital twin technology and predictive analytics could help reduce unplanned downtime and support more continuous imaging operations. The broader field of AI for Operations applies the same principle: using real-time data to anticipate problems rather than respond to them.
Four operational benefits for imaging departments
Predictive services can support imaging workflows in four ways, according to GE HealthCare.
More predictable schedules: Planned interventions during low-utilization windows like evenings or weekends help maintain consistent scheduling blocks, eliminate last-minute disruptions, and optimize staff resource allocation.
Fewer patient cancellations: Forecasting performance degradation weeks in advance gives teams the lead time to trigger proactive interventions. Patients can receive their scans as scheduled, which can drive up satisfaction scores and reduce the administrative burden of emergency rescheduling.
Confidence in system performance: When frontline clinical teams know their imaging fleet is continuously monitored, they can shift cognitive energy to clinical decision-making. Removing the anxiety of technical interruptions matters in high-stakes emergency and high-acuity settings.
Strategic collaboration with service teams: Predictive services can change the relationship with the OEM. Instead of interacting exclusively during a hardware crisis, teams can engage in regular, data-driven dialogues about fleet health, usage trends, and optimization strategies.
For in-house HTM and biomed teams, the impact can be immediate. 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 GE HealthCare's own teams. With OnWatch Predict, they receive direct, automated notifications of emerging issues, helping them act quickly and schedule repairs during low-utilization windows.
Why this matters for healthcare professionals
For radiology leaders, technologists, and biomed teams, predictive maintenance changes the daily reality of imaging operations. Instead of managing crises, teams can operate with early visibility and control over when interventions happen. The value becomes measurable across utilization, workflow continuity, patient access, and service efficiency. As AI-driven condition monitoring matures across AI for Healthcare, imaging departments that adopt predictive service models may see fewer canceled appointments, more on-time starts, and a more reliable experience for both care teams and patients.
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