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Aidoc Staff

What Happens When Radiologists Become More Efficient? 

Care Coordination, Workflow
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There’s a concept from economics that deserves more attention in healthcare.

Known as Jevons Paradox, it describes an observation economists have made for over a century: when something becomes easier, faster, or less expensive to use, people find more ways to use it. Efficiency expands access, and expanded access drives greater demand.

This same pattern is emerging in healthcare, a trend that Aidoc CEO Elad Walach recently highlighted. Today, clinical AI is helping health systems make better use of limited resources. With it, clinicians can identify findings sooner and care teams can respond faster. In turn, more patients can receive treatment.

You can see this playing out today across radiology in particular. Imaging volumes have been rising for years while many health systems face radiologist shortages. The good news, though, is that clinical AI is helping existing radiologists manage growing demand more effectively.

And as radiologists become more efficient, the need for their expertise only grows. Earlier this year, NVIDIA CEO Jensen Huang noted that radiologists are more in demand than ever before. He argued that reading scans is only one part of the job. Diagnosing disease and helping patients get the right care will always require human expertise.

Clinical AI Designed to Support, Not Replace

Against that backdrop, conversations about AI still center on replacement. But many clinical AI solutions, like Aidoc’s, were never developed to substitute radiologists. They were developed as a tool for them to manage growing workloads and focus their attention where it’s needed most.

For example, Aidoc can flag suspected conditions and surface them to the radiologist’s attention, enabling faster triage that can impact patient outcomes. A patient whose coronary artery calcification is identified at an earlier stage, for example, may begin treatment months sooner. A pulmonary embolism identified quickly — and a Pulmonary Embolism Response Team activated automatically — can lead to faster intervention and follow-up care, and so on.

Health systems implementing AI frequently report outcomes that reinforce this. At St. Luke’s Health System, for example, leaders referred to AI as a “game-changer” for managing radiologist shortages and triaging patients. At Lehigh Valley Health Network, leaders have described AI as helping clinicians treat patients more quickly than ever before.

The Radiological Society of North America also highlighted AI as a tool that can help radiologists navigate data, improve workflows, and support decision-making. As adoption expands, the conversation must center on how AI and radiologists can work together. Ultimately, the impact of clinical AI should be measured not only by efficiency, but by how many patients receive timely care.

When Efficiency Expands Access

Today, it’s clear that greater efficiency does not eliminate the need for clinical expertise; it expands the number of patients who can benefit from it.

That expertise must always be at the center of patient care. Radiologists, in addition to specialists across the healthcare system, all play a critical role in improving patient outcomes. AI can help surface information and accelerate workflows, but patients still need experts to confirm findings, interpret data, provide context, make decisions, and coordinate care.

Healthcare has always struggled to deliver limited expertise to a growing number of patients. If clinical AI can extend the reach of that expertise, more patients may receive timely diagnoses and the help they need to live healthier lives. And that may prove to be one of AI’s most important contributions to healthcare overall.

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Aidoc Staff

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