Aidoc

Aidoc provides an enterprise AI platform (aiOS) that analyzes medical imaging — CT, MRI, X-ray — to flag critical conditions like pulmonary embolism, intracranial hemorrhage, and spinal fractures in real time. $150M Series E at ~$3.5B valuation in 2026. Over 2,500 hospitals deployed. Raised total $500M+. Expanding beyond radiology into cardiology and clinical workflows.

Aidoc is the most scaled clinical AI imaging platform globally, with 18+ FDA-cleared algorithms, $500M+ total funding, and a platform approach that solves the hospital's multi-vendor AI implementation problem.

aidoc.com Imaging & Diagnostics AI

Israelprivate

At a glance

Total raised

$384M

last round series e

Headcount

501-1k

approximate · public range

Headquarters

Tel Aviv

Israel

Founded

2016

10 years active

Status

Private

Imaging & Diagnostics AI

Patents on file

USPTO grants

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Funding history

Total raised

$384M

Rounds tracked

Latest stage

Series E

Date	Stage	Amount	Valuation	Lead investors
Apr 29, 2026	Series E	$150M	—	Goldman Sachs Alternatives
Jul 24, 2025	Series D	$110M	—	TCV, Alpha Intelligence Capital
Jul 13, 2021	Series C	$66M	—	General Catalyst
Dec 16, 2020	Series B	$20M	—	Square Peg Capital
Apr 17, 2019	Series B	$27M	—	Square Peg Capital
Apr 26, 2017	Series A	$7M	—	—
Nov 21, 2016	Seed	$4M	—	Emerge, Magma Venture Partners

Founder backgrounds

Elad Walach

Chief Executive Officer & Co-Founder

Holds a B.Sc. in Mathematics and Physics from the Hebrew University of Jerusalem and an M.Sc. in Computer Science from Tel Aviv University. Before Aidoc, he served as head of algorithmic research in the Israeli Air Force, where he led AI research and deployed mission-critical technologies in high-stakes environments — experience he directly carried into healthcare AI. That background shaped Aidoc’s emphasis on real-time, clinical-grade accuracy across CT, MRI, and X-ray imaging. He was named to Forbes 30 Under 30. Aidoc, founded in 2016, is his first venture; there are no prior exits.

Guy Reiner

Chief Architect & Co-Founder

A graduate of the Israeli military's Talpiot program and a veteran of Unit 8200, where he led cutting-edge software research projects. Holds a B.Sc. in Mathematics and Physics from Hebrew University and an M.Sc. in Mathematics from Tel Aviv University — both summa cum laude. Before stepping into the Chief Architect role, previously served as Aidoc's VP R&D and CISO, helping shape the company's aiOS enterprise platform that integrates AI into radiology workflows at scale.

Michael Braginsky

Chief Technology Officer & Co-Founder

A graduate of the elite Israeli military Talpiot program, Braginsky served in Unit 8200 alongside his co-founders before pursuing a B.Sc. in Physics and Computer Science and an M.Sc. in Computer Science specializing in Medical Image Processing at The Hebrew University of Jerusalem. That academic focus — medical image segmentation and visualization — became the technical foundation of Aidoc. Since co-founding the company in 2016, he has defined a product roadmap covering over 75% of common acute CT pathologies and built one of the largest annotated medical imaging databases globally. Aidoc is his first venture; he has no prior startup exits.

AI bull / bear

Bull case

Aidoc sits on the strongest competitive moat in clinical imaging AI. With 18-plus FDA-cleared algorithms and over 2,500 hospital deployments, no rival matches its breadth or installed base. The aiOS platform approach is the right answer to hospital fragmentation: instead of forcing IT teams to juggle a dozen point-solution vendors, Aidoc delivers a unified orchestration layer that flags pulmonary embolism, intracranial hemorrhage, and spinal fractures from a single integration. That stickiness compounds — every new algorithm deepens the relationship. The $150 million Series E, led by Goldman Sachs Alternatives at a reported $3.5 billion valuation, provides a war chest to widen the gap. Aidoc's stated ambition to build a clinical AI foundation model targeting complex diagnostic decisions by 2030 signals the company is moving from triage to comprehensive decision support. Expansion into cardiology and clinical workflows opens adjacent markets far larger than radiology alone. With $500 million-plus in total funding and a proven commercial engine, Aidoc has the runway and the footprint to become the default operating system for diagnostic AI across the enterprise.

Bear case

Scale does not guarantee defensibility, and Aidoc's $3.5 billion valuation bakes in expectations that may outrun clinical AI's real-world economics. Hospital deployments tell a volume story but obscure depth: many sites run only a handful of algorithms on limited modalities, and AI reimbursement remains patchy and provider-specific. The foundation-model pivot toward complex diagnostic decisions by 2030 is an expensive, unproven bet — generalist clinical AI models face regulatory pathways the FDA has not yet defined, and accuracy thresholds for diagnosis are orders of magnitude harder than triage flagging. Meanwhile, competitors like Viz.ai and RapidAI are entrenched in stroke and vascular workflows, and hyperscalers — AWS, Google Cloud — are partnering directly with health systems to deploy their own models. Aidoc's Series E cash burn on foundational research could accelerate without near-term revenue to match. If hospital capital budgets tighten and AI purchasing consolidates around EHR-embedded solutions from Epic or Siemens, Aidoc's standalone platform risks being squeezed out of the enterprise stack.

Generated by deepseek-v4-pro · May 2, 2026Refreshes quarterly · see disclosures

In the news

MedTech Dive5/1/2026
Aidoc raises $150M to advance clinical AI foundation model
Aidoc raises $150M to develop a clinical AI foundation model targeting all complex diagnostic decisions by 2030.

Watch

Leadership

Elad Walach

Chief Executive Officer & Co-Founder

—

Michael Braginsky

Chief Technology Officer & Co-Founder

—

Guy Reiner

Chief Architect & Co-Founder

—

Arvind Kadaba

Chief Financial Officer

—

Aswin Chandrakantan, MD

President & Chief Commercial Officer

—

Jesse Ehrenfeld, MD, MPH

Global Chief Medical Officer

—

Products

aiOS™
Aidoc's enterprise AI operating system that runs continuously in the background to analyze and aggregate medical imaging data from CT, MRI, and X-ray exams. aiOS orchestrates and governs AI across the entire health system, unifying data and workflows to surface critical findings, activate care teams, and support continued care beyond diagnosis. It enables hospitals to run multiple AI algorithms at scale with seamless integration into existing IT infrastructure.
Radiology AI Solutions
A suite of FDA-cleared AI algorithms that analyze radiology scans in real time to flag suspected acute conditions such as pulmonary embolism, intracranial hemorrhage, spinal fractures, and abdominal pathologies. The solutions prioritize findings directly in the radiologist's workflow, enabling faster triage and treatment decisions across the care continuum.
Vascular AI Solutions
Aidoc's vascular-focused AI applications detect and flag life-threatening vascular conditions including aortic aneurysm, aortic dissection, and pulmonary embolism from CT scans. The solutions automate the identification of incidental and critical vascular findings, alerting care teams to expedite interventions and improve patient outcomes in emergency and routine settings.
Neuro AI Solutions
Designed for neurological emergencies, these AI solutions analyze head CT and MRI scans to identify intracranial hemorrhage, large vessel occlusion, and other acute neuro conditions. The algorithms automatically flag findings and trigger alerts to neurologists and stroke teams, reducing time-to-treatment for time-sensitive neurological events.

Most recent patents

Aidoc Medical Ltd·2026-01-06
Performance Estimation Of Machine Learning Models That Analyze Medical Images
US12518517
Abstract
Performance Estimation Of Machine Learning Models That Analyze Medical Images
US12518517
Abstract There is provided a method, comprising: feeding target metadata parameters of target medical image(s) into a performance estimation machine learning (ML) model, and obtaining a performance metric of a computer vision ML model as an outcome of the performance estimation ML model, wherein the performance estimation ML model is trained on a training dataset comprising records, wherein a record is created by feeding a sample medical image into the computer vision ML model, obtaining an outcome of the computer vision ML model, obtaining a ground truth of the sample medical image corresponding to the outcome, extracting metadata parameters associated with the sample medical image, and wherein the record includes metadata parameters associated with the sample medical image and a ground truth label comprising a performance metric of the computer vision ML model computed based on the ground truth of the sample medical image and the outcome.
View on Google Patents
AIDOC Medical Ltd.·2024-06-18
System For Determining The Presence Of Features In A Dataset
US12014489
Abstract
System For Determining The Presence Of Features In A Dataset
US12014489
Abstract A system for determining whether a dataset including a plurality of cross-sectional images includes a predetermined feature is provided. A first AI receives a dataset including a plurality of cross-sectional images, and analyses the dataset to identify a subset of cross-sectional images of the dataset capable of including the predetermined feature A second AI model receives a first cross-sectional image from the subset, analyses the first cross-sectional image to determine whether the first cross-sectional image includes the predetermined feature, and outputs an indication of whether the first cross-sectional image includes the predetermined feature. A processor is configured to obtain the output from the second AI model, and based on the output from the second AI model indicating that the first cross-sectional image includes the pre-determined feature, determine that the dataset includes the predetermined feature.
View on Google Patents

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