Aidoc Partners With Sol Radiology To Deploy Clinical AI Across Southern California

Aidoc announced a partnership with Sol Radiology to deploy clinical AI across hospitals, outpatient imaging centers, and urgent care facilities in Southern California, the company said.

Sol Radiology has implemented a suite of FDA-cleared Aidoc solutions across its network, embedding multi-condition AI directly into radiologists’ workflows to identify and prioritize critical findings.

The deployment spans multiple care settings, allowing radiologists to surface urgent cases earlier and communicate findings more efficiently to referring physicians. Faster prioritization can reduce delays between imaging, diagnosis, and treatment.

The move comes as radiology groups adopt integrated AI platforms to manage growing imaging volumes and workflow pressure. A recent AIM Media House analysis found that the radiology AI market is consolidating around platform-based systems rather than single-use tools.

Workflow Integration Drives Deployment

Aidoc’s platform, aiOS, combines imaging data, electronic health record (EHR) context, and AI outputs into a single workflow layer for radiologists. This allows clinicians to review prioritized cases within their existing reading environment.

“Aidoc has helped us prioritize urgent findings and move cases through more efficiently, which ultimately means faster, better care for our patients,” said Rahul Nayyar, President at Sol Radiology.

Aidoc said its systems are designed to integrate into clinical workflows rather than operate as standalone tools, a factor that affects adoption in radiology settings. Prior deployments show how AI platforms can be embedded directly into hospital imaging systems to generate real-time alerts.

“Clinical AI only delivers value when it’s embedded in the workflow and trusted by the physicians using it,” said Aswin Chandrakantan, MD, President at Aidoc.

Aidoc reports its platform has been deployed across more than 1,600 medical centers and supports approximately 60 million patients each year.

Focus On Throughput And Patient Flow

Radiology workflows are often constrained by the time required to review large volumes of imaging studies. AI-based triage systems are designed to flag high-risk cases and move them to the top of worklists, allowing clinicians to act sooner.

Sol Radiology said the deployment is intended to streamline how critical findings are identified and communicated, improving coordination between radiologists and referring physicians.

AIM Media House reporting on healthcare AI deployments shows that similar systems have reduced diagnosis times and improved prioritization in imaging workflows.

The use of multi-condition AI systems also reduces the need for separate tools for individual findings, allowing radiology groups to manage multiple clinical scenarios within a single platform.

The collaboration extends Aidoc’s footprint into community-based imaging settings, where organizations are adopting enterprise AI systems to manage workflow efficiency and patient throughput across distributed care environments.