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Blog
Aidoc Staff

Closing the Gap Between Image and Action

Care Coordination, System Integration, Workflow
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Over the past decade, clinical AI has improved how radiologists manage time-sensitive issues. By surfacing critical findings sooner, these tools are shortening the time between diagnosis and treatment. I think we can all agree that’s a good thing.

It’s no surprise then that AI adoption is accelerating across healthcare. NVIDIA’s 2026 State of AI in Healthcare report found that 70% of healthcare and life sciences organizations are now actively using AI, up from 63% the year before. But as AI helps identify more findings upfront, attention is increasingly shifting to what happens next.

Why Diagnosis Is Not Enough

Once a finding is identified, the information still needs to reach the right people. Specialists need to weigh in, and multiple teams may need to coordinate care. For many patients, delays in treatment occur not because a finding was missed, but because communication and coordination break down.

The work doesn’t end when a critical finding appears on a scan. A stroke diagnosis may trigger an immediate response from neurology. A pulmonary embolism may require consultation with cardiology, critical care, and emergency medicine. In many cases, multiple clinicians must review the findings and align on treatment before care can move forward.

Reducing Delays After the Confirmed Finding

Lehigh Valley Health Network confronted this challenge while building its Pulmonary Embolism Response Team (PERT), which includes clinicians from emergency medicine, radiology, cardiology, and critical care.

For patients with acute pulmonary embolisms, treatment depends on specialists reviewing the case and coordinating care in a matter of minutes rather than hours.

Before implementing AI, those conversations depended on pages, phone calls, and multiple systems. Today, the right clinicians are notified alongside the imaging, allowing specialists to review the case and coordinate care more quickly. As Dr. Singh explained, the goal was to close the gap between image acquisition and clinical activation.

One case shows what that looks like in practice. After receiving an overnight alert for a patient with a saddle pulmonary embolism, Dr. Singh reviewed the scan remotely, activated the care team, and had the patient transferred within minutes. The procedure began in less than 30 minutes. Before, he noted, the process could have taken hours.

The lesson extends well beyond pulmonary embolism care. A diagnosis can only influence treatment when the right people receive the information quickly enough to act.

Earlier Detection Changes the Challenge

As health systems identify and treat more patients, the need for coordination also grows. This dynamic reflects a broader concept known as Jevons Paradox: when something becomes more efficient, demand often increases.

Aidoc CEO Elad Walach recently pointed to this phenomenon in healthcare. As AI helps clinicians identify findings sooner and speeds the path to treatment, more patients can receive care.

That is a positive development, but it also creates new demands. More patients entering treatment means more follow-up and more coordination across teams.

The value of earlier detection depends on a health system’s ability to move patients from diagnosis to care without delays.

From Diagnosis to Action

Temple Health CEO Michael Young recently argued that the value of AI extends beyond image interpretation. For Temple, one of the biggest benefits is helping findings reach the clinicians responsible for treatment more quickly.

As Young noted, the greatest value comes when clinicians can act on information sooner.

Clinical AI has already helped radiologists identify important findings faster. Increasingly, it is also helping health systems communicate findings, coordinate care, and reduce delays between diagnosis and treatment.

Radiologists have always played a central role in moving patients from diagnosis to treatment.

Ultimately, the future of clinical AI will be measured not only by what it helps clinicians find, but by how effectively it helps patients receive timely care.

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