AI Imaging CDS Market Map: Unblocking the Radiology Department
This is part of Elion’s weekly market map series where we break down critical vendor categories and the key players in them. For more, become a member and sign up for our email here.
As the number of providers in the U.S. increases (albeit, not fast enough to keep pace with the rate of new patients), the number of radiologists has not kept up. Simultaneously, providers are ordering more studies per physician—estimated at an increase of 5-7% annually—resulting in an urgent need for more radiologists, or alternatives to drastically improve radiologists’ efficiency without negatively impacting accuracy.
AI imaging clinical decision support (CDS) solutions use artificial intelligence—primarily machine learning and deep learning techniques—to assist in reading, interpreting, or prioritizing medical images. These systems may flag critical abnormalities (e.g. hemorrhages, suspicious nodules), generate automated radiology impressions, or even manage patient follow-up schedules. While traditional radiology software focuses on viewing and storing images, AI imaging CDS goes a step further by interpreting or triaging them, effectively acting as an assistant to the radiologist or clinician.
AI in Radiology Over the Decades
AI in medical imaging is not new—computer-aided detection (CAD) programs have existed since the late 1990s, initially used in mammography to highlight suspicious areas for review. However, it wasn’t until deep learning breakthroughs (around 2012–2015) that algorithms could surpass or closely match human-level performance in certain image recognition tasks. Regulatory bodies like the FDA began to clear AI imaging tools more rapidly in the late 2010s, paving the way for a wave of commercial AI products targeting stroke detection, diabetic retinopathy, lung nodule detection, and more.
The current crop of AI imaging CDS solutions typically rely on convolutional neural networks (CNNs) for object detection and classification. The core steps typically include:
Preprocessing: Raw images (CT, MRI, X-ray) are converted into standardized formats.
Inference: The CNN or deep learning model analyzes images for anomalies.
Workflow integration: Abnormal scans get flagged in the PACS viewer, or a separate AI console or mobile app alerts radiologists/clinicians in real time.
Reporting/follow-up: Some solutions also auto-generate preliminary impressions and may track incidental findings for long-term patient management.
Thanks to more mature interoperability standards these tools increasingly plug directly into existing healthcare IT systems. The technology is evolving rapidly; newer algorithms can combine imaging data with electronic health records and lab results to predict disease progression or personalize treatment.
Differentiating AI Imaging CDS Vendors
Vendors in this space fall into a few key categories:
Blackford Analysis Platform, CARPL, and deepcOS function as AI app marketplaces and aggregators for a variety of task-specific AI models.
HeartFlow, Cleerly, and Ligence focus specifically on cardiac imaging analysis.
Aidoc, RapidAI, Viz.ai One, Qure.ai, and Envisionit Deep AI each focus on specific critical conditions and assist in identifying and triaging them.
Rad AI Reporting and Continuity and Eon Patient Management, on the other hand, focus more on follow-up and workflow management, helping to track incidental findings and pre-populating reports for radiologists to finalize.
iCAD’s ProFound Breast Health Suite and Lunit INSIGHT utilize AI to improve mammography accuracy, enhance the detection of abnormalities, and predict treatment responses. Their focus extends to reducing false positives in screenings and aiding in personalized care strategies.
Icometrix specializes in analyzing brain MRI and CT scans for conditions like multiple sclerosis and Alzheimer’s disease to better quantify insights and the tracking of disease progression.
What’s Next for AI Imaging CDS?
As more tools win regulatory clearance and providers amass positive ROI data, broader adoption seems inevitable. However, challenges remain around algorithm generalizability, data privacy, and ensuring clinicians remain engaged without developing “automation bias.” Despite this, the need for increased efficiency is likely sufficient to overcome these hurdles.
Future solutions will likely encompass predictive analytics—combining imaging, EHR data, and genomics to forecast disease trajectories. We can also expect deeper workflow integration; the next wave of platforms may unify image interpretation, reporting, and triage with direct patient scheduling and remote monitoring.