Aidoc
Brings findings into a shared workflow for radiology, cardiology and neurovascular teams.
Aidoc Funding History
Aidoc develops clinical AI software that analyzes medical imaging and supports care coordination. Its platform connects image-analysis capabilities with clinical workflows, serving healthcare organizations that use AI to help manage findings and coordinate the next steps in patient care.
Company Overview
Aidoc Funding Timeline
Series E
$150M
Aidoc planned to extend its CARE model to more conditions and improve the workflow from CT or X-ray images to draft reports. The Series E followed the earlier CARE growth financing.
Led by Growth Equity at Goldman Sachs Alternatives. Participation included General Catalyst, SoftBank.
Source · aidoc.com ↗Strategic Financing (CARE Foundation Model)
US$150 million
The financing backed development of Aidoc’s CARE clinical foundation model and included a $40 million revolving credit facility.
Led by General Catalyst, Square Peg Capital. Participation included NVentures.
Source · aidoc.com ↗Series D
US$110 million
Aidoc framed the financing around the operational pressures facing health systems and the role of clinical AI in helping them manage those demands.
Led by TCV, Alpha Intelligence Capital.
Source · prnewswire.com ↗Series C
US$66 million
The round followed Aidoc’s deployment with Radiology Partners. Aidoc planned to expand its medical imaging platform across specialties and make it easier for health systems to deploy.
Led by General Catalyst. Participation included TCV.
Source · prnewswire.com ↗Series B Extension
US$20 million (Series B total US$47M)
This was an extension of the existing Series B. The announcement cited growing demand for AI tools used in radiology.
Led by Square Peg Capital.
Source · prnewswire.com ↗Series B
US$27 million
Aidoc said it would grow its technical and commercial teams to meet demand for its AI tools for radiologists.
Led by Square Peg Capital. Participation included General Catalyst, Alpha Intelligence Capital, TLV Partners, TCV.
Source · prnewswire.com ↗
Aidoc Founding Team
Elad Walach, Michael Braginsky, and Guy Reiner, graduates of Israel’s Talpiot program, founded Aidoc to bring AI into radiology workflows. Walach led the company as CEO, Braginsky led engineering as CTO, and Reiner led research and development.
Aidoc Products
Aidoc Company Development
Foundation Models for Clinical Imaging
Aidoc broadened its imaging and care-coordination work with CARE, a healthcare foundation-model effort. That work gives its clinical AI program a shared model initiative alongside the software already used to analyze imaging and coordinate follow-up.
Aidoc Competitive Landscape
Different Entry Points into Hospital Workflows
Aidoc enters through imaging, where an AI finding may need to reach a clinical team quickly. Abridge enters through the visit conversation and helps produce a structured note. Hospitals evaluating both should treat them as serving different points in the care process.
FAQ
What is the difference between imaging triage and a final diagnosis?
Triage helps prioritize review. A diagnosis involves clinical interpretation and responsibility; an AI flag should not be treated as a replacement for that process.
Does a radiology AI platform have one approval for every use?
Regulatory permissions are tied to particular products, indications and jurisdictions. They should be checked individually rather than inferred from a platform description.
Why does hospital workflow affect imaging AI adoption?
Alerts must reach the right care team and fit existing imaging systems. An algorithm’s performance alone does not show whether the deployment improves care.
How should Aidoc’s later-stage financing be interpreted?
It records a capital event for the company. It does not by itself establish revenue, profitability or clinical outcomes.
Can funding for imaging AI be compared directly with drug-development funding?
The business models, validation requirements and development costs differ. Round size alone is a weak basis for comparing their operating progress.
What evidence matters when assessing an imaging AI deployment?
Look for the specific intended use, relevant validation population, false-alert burden and performance after integration into clinical practice.