Healthcare systems are not constrained by lack of technology. They are constrained by staffing shortages, rising demand, and limited capacity. In imaging, those pressures are changing how AI is evaluated and adopted.
What this conversation revealed
AI in imaging has crossed a threshold according to Roland Rott, CEO and President of Imaging at GE Healthcare. AI is no longer an efficiency add-on, it has become a requirement to maintain access and operational stability. To accelerate AI capability, healthcare needs to unleash the potential of dormant health data.
From optional tool to operational requirement
AI adoption in imaging is now driven by necessity. Rott explained it this way: “There is tremendous interest and growing trust [in AI]. There is an openness from healthcare practitioners, but also healthcare systems to say without AI, I cannot imagine running my operation.”
That is a significant change in the attitude towards AI in healthcare. It was only a few years ago where AI was viewed with skepticism as claims far exceeded actual capability. However, with the reality of tight staffing and growing waitlists, AI with it’s power to automate administrative tasks and remove friction from clinical workflows has become foundational.
“Radiology teams need to save time,” said Rott. “As a leader, I cannot do nothing. It is imperative that I give my users the tools to help them care for patients faster.”
Turning dormant data into capacity
AI is fueled by data and healthcare has a lot of it. Rott, however, pointed to an interesting fact about healthcare data: “We learned that 97% of all healthcare data has been unused until recently.”
To improve AI algorithms, researchers and companies are looking to these previously untapped data sources in healthcare.
“The beauty of AI is that it is able to deal with large amounts of data,” said Rott. “We can apply smarter solutions to various challenges. One can be [clinical] workflow optimization.”
GE Healthcare, for example, is tapping into DICOM data with AI tools to help optimize the time for each appointment. The system looks at prior histories of that patient and is able to suggest either to lengthen or shorten the time. This means that patients who need more time are not rushed while those that can be seen quicker create openings for other patients.
Even better, there is no extra burden on staff. GE Healthcare’s AI tools do most of the work.
For more on this capability, be sure to read/watch GE Healthcare Uses DICOM Data and Image Clarity to Improve Radiology Workflows.
What Healthcare IT Leaders Are Asking
1. Why is AI becoming a requirement in radiology operations?
Staffing shortages, rising imaging volumes, and patient access pressures have outpaced what manual workflows can support. AI is increasingly used to absorb operational strain by reducing wasted time, automating routine tasks, and improving throughput without adding staff.
2. What problems is AI actually solving for radiology departments?
AI is being applied to reduce exam duration, optimize appointment length, surface workflow inefficiencies, and help teams manage more patients with the same or fewer resources. The value shows up in access, throughput, and staff workload, not just analytics.
3. How does AI use imaging and DICOM data to improve capacity?
By analyzing prior exam data, patient history, and scan characteristics, AI can recommend more accurate appointment lengths. This reduces overbooking and underutilization, helping radiology departments open capacity without extending hours.
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