Aidoc, which uses AI to help radiologists analyze medical scans, raises $27M Series B led by Square Peg Capital, bringing its total raised to $40M
Context & Ripple Effects
In April 2019, Aidoc's [[a:957944|Series B looked like a mid-stage bet on a narrow wedge: AI that flags urgent findings on medical scans so radiologists can prioritize them]]. Square Peg Capital leading the $27M round put the Tel Aviv company at $40M raised, still selling into a radiology market where most hospitals had no clinical AI in production.
The arc since then shows what that wedge became: Aidoc raised $66M led by General Catalyst two years later, then a $110M Series D in 2022 took total funding to about $250M, and by 2026 a $150M Series E pushed the total to $520M. The 2019 round is the entry point of one of the best-capitalized runs in clinical imaging AI.
First-order effects
- Aidoc gets runway to scale its scan-triage product beyond early hospital deployments, with Square Peg Capital now holding a lead position in a category it would back again years later.
- Radiology departments evaluating AI triage gain a funded vendor whose pricing and deployment model no longer depends on raising another bridge just to keep selling.
Second-order effects
- Rival scan-analysis startups face a competitor with fresh capital to bundle more findings and modalities per contract, pressuring them toward their own larger rounds or consolidation.
- Square Peg's continued involvement — it co-led Aidoc's $150M round in 2025 alongside General Catalyst and NVIDIA's NVentures — signals that early clinical-AI backers treated imaging triage as a repeatable investment thesis rather than a one-off bet.
Third-order effects
- The pattern across these rounds points to clinical AI consolidating around platform vendors whose software sits in the reading workflow itself, shifting purchasing power from individual radiology groups to health systems — four major U.S. health systems participated directly in Aidoc's 2025 round.
- If capital keeps flowing at this scale, regulatory clearance and hospital integration become the moats in medical imaging AI, not model accuracy alone, favoring incumbents with installed deployments over new entrants.
The trend: Clinical imaging AI is scaling from single-purpose scan-flagging tools into workflow-wide decision-support platforms, with funding rounds growing an order of magnitude as health systems become direct investors.