Twelve U.S. Health Systems and Aidoc Unite to Confront America’s Diagnostic Capacity Crisis

NEW YORK and CLEVELAND - Twelve U.S. health systems have formed the Diagnostic AI Consortium with Aidoc to harness diagnostic AI to improve the safety, quality and speed of diagnosis, and contribute the evidence and governance lessons they build to the broader effort already underway across medicine.

The members collectively care for nearly 20 million patients annually. Members include Advocate Health, Cedars-Sinai Health System, Hartford Healthcare, Houston Methodist, Mercy, Mount Sinai Health System, Northwell Health, Northwestern Medicine, Sutter Health, University of Florida Health, University Hospitals of Cleveland, and WellSpan Health, and continues to grow.

The Diagnostic AI Consortium launches at a moment when AI is rapidly expanding what is possible across industries. But diagnosis presents a fundamentally different challenge. Rather than generating content or answering questions, diagnostic AI must detect subtle clinical signals across imaging, pathology, laboratory data and the medical record, helping physicians recognize disease earlier and act faster.

In medicine, where every missed or delayed diagnosis can change a patient's outcome, that requires AI purpose-built for clinical decision-making, rigorously validated in clinical practice, FDA-cleared, and designed to improve measurable patient outcomes. Every day, physicians must synthesize expanding volumes of information across imaging, pathology, laboratory data and the medical record, often under mounting operational pressure. Additionally, AI performance must be continuously monitored for drift and bias across patient populations, sites and scanners, a discipline still rare in AI broadly.

"For a long time, the industry treated speed and safety as opposing forces in AI: Silicon Valley's 'move fast and break things' against medicine's 'first, do no harm,'" said Elad Walach, CEO and co-founder of Aidoc. "This Consortium is built on the belief that it can be done the right way, guided by two principles: iteratively and together. Twelve of the country's leading health systems holding themselves to shared standards for how diagnostic AI is evaluated and governed is how we earn the right to scale it, and how we shorten the time from scan to diagnosis for every patient."

Imaging illustrates this challenge especially clearly. Interpretation turnaround times for outpatient imaging more than doubled between 2014 and 2023, according to the Harvey L. Neiman Health Policy Institute. The workforce is thinning at the same time: radiologists have been leaving the field at a 50% higher rate since 2020, and Neiman researchers project the shortage will persist through 2055 without deliberate action, as an aging population drives imaging demand faster than new radiologists enter the workforce.

The Diagnostic AI Consortium exists to improve AI-enabled diagnostic workflows aimed to measurably improve safety, quality and speed to diagnosis across diverse health systems. No single health system can do that alone: proving that discipline at scale is central to the Consortium's mandate.

The Consortium will: 1) co-design future AI-enabled diagnostic workflows intended to prioritize the right cases, accelerate interpretation, and deliver critical results sooner to improve patient flow in real-world clinical settings;; 2) measure their impact on safety, quality and speed to diagnosis across member sites; and 3) turn what works into shared implementation and governance practices any health system can adopt, inside the Consortium or beyond it.

"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," said Leonardo Kayat Bittencourt, MD, Vice Chair of Innovation at University Hospitals. "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."

The Consortium is launching as health systems move beyond isolated AI applications toward enterprise-wide diagnostic AI, and its members share a view of what that shift makes possible: a health system where a critical finding starts moving through the care pathway soon after a scan is complete, where the sickest patients are correctly prioritized, and where a diagnostic workup that stretches across days is completed in hours. The Consortium's findings are intended to augment and advance initiatives already underway across the industry, from Aidoc and its customers to regulators, medical societies, and health systems building their own programs.

With AI running in nearly 2,000 hospitals and analyzing 60 million patient cases a year, Aidoc brings operational experience at a scale no single institution can match. Aidoc will provide the Consortium's technical infrastructure through CARE™, its clinical AI foundation model, and aiOS™, its enterprise AI operating system, which manage deployment, workflow integration and post-deployment monitoring.

The Consortium expects to share its initial results in 2027.