Twelve US Health Systems Join Aidoc To Form Diagnostic AI Consortium

Twelve US health systems have formed the Diagnostic AI Consortium with clinical AI company Aidoc to develop and evaluate AI-enabled diagnostic workflows across their organizations.

The consortium, announced on August 11, includes 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. The members collectively care for nearly 20 million patients annually, according to Aidoc's announcement.

The group plans to co-design AI-enabled workflows, measure their impact across member sites and develop shared implementation and governance practices. Aidoc expects the consortium to continue growing and share initial results in 2027.

The initiative comes as health systems face rising demand for diagnostic services and pressure on clinical capacity. A Harvey L. Neiman Health Policy Institute study found that the time between outpatient imaging and interpretation increased 113% from 2014 to 2023, based on 2.6 million Medicare fee-for-service imaging studies.

For CT scans, turnaround time increased 318% over the period, while MR increased 256%, ultrasound 140% and radiography and fluoroscopy 63%, according to the institute.

Health Systems Move Toward Shared AI Evaluation

The consortium's focus extends beyond deploying individual AI tools. Its members will evaluate how AI can prioritize cases, accelerate interpretation and move critical findings through care pathways while measuring effects on safety, quality and time to diagnosis.

That approach reflects a growing emphasis on evaluating clinical AI after deployment. In May, the American College of Radiology (ACR) approved its first practice parameter for imaging AI, covering tool selection, local testing, governance and ongoing monitoring for performance drift and safety issues.

The ACR also launched Assess-AI, a registry and data service designed to monitor AI performance across clinical practices. Its framework supports measurement of AI outputs against radiology report-derived data and allows sites to compare performance over time.

Hospitals have increasingly used AI for specific clinical tasks rather than autonomous diagnosis, as outlined in recent hospital AI workflow deployments. Advocate Health, one of the consortium members, has already used Aidoc's imaging AI to flag incidental pulmonary embolisms in routine outpatient imaging, while a separate deployment with Sol Radiology shows how Aidoc's tools can be embedded directly into radiologists' workflows through clinical AI deployment across Southern California.

Governance Becomes Part of Diagnostic AI Strategy

The consortium's work comes as medical organizations place greater emphasis on physician oversight and evidence for clinical AI. In June, the American Medical Association (AMA) adopted policies calling for AI used in clinical decision support to remain under physician oversight and support evidence-based care rather than replace physician judgment.

Aidoc said its technical infrastructure for the consortium will include CARE, its clinical AI foundation model, and aiOS, its enterprise AI operating system. The company said its technology is deployed in nearly 2,000 hospitals and supports approximately 60 million patients annually.

Elad Walach, CEO and Co-Founder of Aidoc, said the consortium would use shared standards for evaluating and governing diagnostic AI, with the goal of establishing evidence for scaling the technology.

The company said diagnostic AI must work with clinical signals across imaging, pathology, laboratory data and medical records. It also said performance needs to be monitored for drift and bias across patient populations, sites and scanners.

Radiology is a major pressure point. Neiman HPI research projects that the US radiologist shortage will persist through 2055 under current conditions, with radiologist supply and imaging demand both expected to grow over that period.

The consortium will initially focus on measuring what works across diverse health systems. Its first results are expected in 2027.