Aidoc, which uses AI to help doctors make real-time clinical decisions, raised $150M led by General Catalyst and Square Peg, bringing its total funding to $370M
Advancing the Frontier of Clinical Intelligence — Table of contents Naomi Diaz / Becker's Hospital Review : 4 health systems join $150M funding round for AI company Fred Pennic / HIT Consultant : Aidoc Secures $150M to Accelerate CARE™ Foundation Model and Clinical AI Deployment Anthony Vecchione / Mobi Health News : Aidoc secures $150M for clinical-grade foundation model Katie Adams / MedCity News : Aidoc Rakes In $150M for Its Clinical Decision Support AI X: @aidocmed : Today, we're excited to announce a $150M investment, co-led by General Catalyst and Square Peg, with participation from NVIDIA's NVentures and four major U.S. health systems. This brings Aidoc's total funding to $370M, a milestone that marks the beginning of a new chapter in our [image] LinkedIn: Rotem Geslevich : 🚨 Today we announced a $150M investment (total now $370M), focused on advancing CARE™ 🧠 — our clinical-grade foundation model …
Context & Ripple Effects
Aidoc has progressed from AI tools for radiologists reviewing scans to a broader clinical-decision platform. Its earlier $110M Series D for imaging AI had already brought total funding to roughly $250M, following a $27M Series B led by Square Peg.
This round pairs returning financial backers with participation from four U.S. health systems and NVIDIA’s NVentures. That combination matters because clinical AI deployment depends not just on model development but on adoption inside provider workflows.
First-order effects
- Aidoc gains $150M to advance its CARE™ foundation model and deploy its clinical decision-support software, lifting disclosed total funding to $370M.
- General Catalyst and Square Peg deepen their exposure to Aidoc, while participating health systems gain a closer relationship with a supplier they may help validate and deploy.
Second-order effects
- Aidoc’s larger funding base raises pressure on clinical-AI rivals to show both hospital adoption and credible paths from narrow imaging tools to broader workflow support.
- Health-system participation makes provider partnerships a more important competitive asset: vendors will need clinical deployment evidence alongside model claims to win enterprise buyers.
Third-order effects
- If this financing pattern persists, clinical AI may consolidate around well-capitalized platforms able to fund model development, integration and validation simultaneously, rather than around standalone imaging features.
- The practical differentiator will increasingly be whether AI can be embedded in real-time care workflows with provider support, not simply whether it can analyze medical images.
The trend: Clinical AI funding is shifting toward platform-scale companies that combine foundation-model development with health-system deployment partnerships.