Oren Weiner

What’s One Thing Many of America’s “Great” Hospitals Have in Common? Clinical AI

Steve Jobs once wisely said that “innovation is the ability to see change as an opportunity not a threat.” We couldn’t agree more. In a world where healthcare institutions are fighting battles on numerous fronts to keep delivering quality care, innovation becomes an opportunity to push on and rise above.

Becker’s Hospital Review recently released their list highlighting the “Great Hospitals in America.” Each of the facilities “are renowned for clinical excellence, patient safety, innovation efforts, research and education, patient satisfaction and more.” 

One thing many of these institutions have in common is early adoption of clinical AI solutions Below are a few highlights from Aidoc partners on the “Great” list helping to grow the body of evidence and utility that continues to quantify the many positive clinical and financial outcomes of AI.


Out of the Golden State, Cedars-Sinai was one of the first to partner with Aidoc, leading to a number of collaborative clinical studies on AI with Aidoc—from diagnostics to care coordination. In 2022, a research team at Cedars-Sinai analyzed the correlation between notification of pulmonary embolism response team members at the same time as diagnostic radiologists and time to thrombectomy.

In their clinical study, the team found that by notifying the PE response team at the same time, the AI could help them reduce the time to thrombectomy by up to 10 hours and ultimately reduce patient length of stay by 3 days.

Atlantic Health (Overlook Medical Center)

Commencing with over 90 users of Aidoc solutions, Overlook Medical Center deployed Aidoc to support its radiology teams with triaging, among other reasons. In one case, Dr. Klein, Chairman of Radiology and Radiation Oncology at Overlook Medical Center, shared:

“One case example I think that highlights the success of our deployment of Aidoc is a story of a young man who had a somewhat remote history of cancer and came in for a routine follow-up examination.

And this one individual who was just having a routine follow-up examination had an embolus in the lung base that was identified by the Aidoc solution. Because it was identified by the Aidoc solution, the radiologist was notified almost immediately of a potentially positive life-threatening finding on that CT scan.

As a result, rather than reading the next ER case or the next ICU case, the radiologist read his case next, saw that it was positive, validated the findings from the solution and then was able to contact the patient’s physician and the patient was begun on anticoagulant therapy within hours. Without Aidoc’s solution that case may not have been read til later that day or maybe even the next day.”

University of Texas Southwestern

Patient capture plays a critical role in supporting the reduction in time to therapy and improvement of care. In exploring the impact of AI on workflows to help reduce the time to deliver anticoagulation to patients, a clinical research team at UTSW deployed Aidoc’s iPE solution in AI-driven workflows. They found a 13-hour reduction in time to retrieval of prescription for anticoagulant therapies for patients suffering from a pulmonary embolism.

University of Rochester Medical Center (URMC)

From the micro to the macro, URMC commenced their AI journey with stroke, deploying the intracranial hemorrhage (ICH) Aidoc AI solution. In a clinical study on the correlation between report turnaround time and AI deployment, the clinical research team identified a 36% reduction in time to reporting positive cases of ICHs.

After realizing the impact of Aidoc AI solutions, URMC leveraged the aiOS to scale up their AI operations from AI analyzing 1,687 cases per month to 18,307 cases per month, providing a blueprint for other healthcare organizations to achieve scale with her AI strategy.

Carle Foundation

Stroke care centers have been looking to innovation as a means to improve their response time and patient treatment. At Carle Foundation hospital, Dr. Brian Mason and colleagues deployed medical imaging AI and care coordination workflows to expedite care and improve outcomes for stroke patients. Dr. Mason describes one case:

“The module identified an LVO and immediately notified the radiologist… we went into the operating room immediately  and pulled out a 3-centimeter clot out of her brain with a reperfusion catheter. Because we were able to intervene so fast on this patient, she was able to leave the hospital with outpatient rehab in three days.”

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Oren Weiner