12 leading hospital systems team to advance radiology AI
Twelve prominent U.S. hospital systems have joined forces to create a new consortium focused on improving the safety, quality and speed of diagnostic care through artificial intelligence.
The consortium will focus on developing AI-backed workflows that prioritize appropriate cases, accelerate imaging interpretation times and deliver critical findings sooner. Members will measure the impact of reengineered workflows across health systems and replicate successful approaches, sharing key data to help others seeking to implement AI. This is particularly relevant in radiology, as imaging volumes continue to rise alongside a shortage of skilled radiologists.
The partnership comes at a time when thousands of health organizations have already deployed AI in clinical workflows, with thousands more likely to follow. As such, leaders of the group believe it is time to look beyond initial deployment strategies toward integrating AI across broader clinical workflows.
“Joining the Diagnostic AI consortium reflects our belief that this next generation of AI in radiology will only reach its full potential through shared expertise and accountability across institutions and the industry,” Leonardo Kayat Bittencourt, MD, vice chair of innovation at University Hospitals of Cleveland, said in a news release Aug. 11. “No single center, however large or reputable, can capture the diversity of imaging, disease, and clinical context needed to build and deploy models that generalize safely and at the scale the world needs. We are proud to join this effort, and excited to contribute our expertise to help shape not only the future of radiology, but the future of healthcare itself."
Along with University Hospitals, others in the consortium include Advocate Health, Cedars-Sinai, Hartford HealthCare, Houston Methodist, Mercy, Mount Sinai, Northwell, Northwestern Medicine, Sutter, the University of Florida, and WellSpan, with more expected to join. Collectively, these health systems care for nearly 20 million patients per year.
The consortium is intended to help health systems establish a more coordinated approach to AI deployment. Rather than focusing solely on whether an individual algorithm performs accurately, the initiative will examine if AI-enabled workflows actually improve clinical outcomes. The goal is to connect the dots between AI-generated findings to the next steps in a patient's care.
Aidoc is partnering with the consortium to provide technical infrastructure through its CARE clinical AI foundation model and aiOS enterprise AI operating system. The company says its technology is currently deployed across nearly 2,000 hospitals and analyzes approximately 60 million patient cases annually.
The consortium expects to release its initial findings in 2027.
In addition to her background in journalism, Hannah also has patient-facing experience in clinical settings, having spent more than 12 years working as a registered rad tech. She began covering the medical imaging industry for Innovate Healthcare in 2021.