Aidoc was founded in 2016 by Elad Walach as the CEO,[5] Michael Braginsky as the CTO and Guy Reiner as the VP. In April 2017, the company raised $7M, led by TLV Partners,[6] and in April 2019, the company raised another $27M, led by Square Peg capital.[7] There have been several additional rounds of funding as well, bringing Aidoc's total investment to $370M as of July 2025.[8]
In January 2020, the system for detecting large-vessel occlusions (LVOs) in head CTA examinations obtained FDA clearance.[12][13][14]
In October 2024, it was reported that Aidoc is working with NVIDIA to develop a framework for deployment and integration of artificial intelligence tools in healthcare. The Blueprint for Resilient Integration and Deployment of Guided Excellence (BRIDGE) is a guideline to facilitate AI adoption in the healthcare industry. [15][16][17][18]
Products and market
Aidoc has developed a suite of artificial intelligence products that flag both time-sensitive and time-consuming (for the radiologist) abnormalities across the body. The algorithms are developed with large quantities of data to provide diagnostic aid for a broad set of pathologies. The company offers an array of algorithms that span across the body, including for intracranial hemorrhage, spine fractures (C, T & L), free air in the abdomen, pulmonary embolism, and more. It developed "Always-on AI", a term coined by Elad Walach that refers to a type of artificial intelligence that is "Always-on—constantly running in the background and automatically analyzing medical imaging data, identifying urgent findings, and sparing radiologists from "drowning" in vast amounts of irrelevant data.[19][20]
A clinical study on Aidoc’ accuracy of deep convolutional neural networks for the detection of pulmonary embolism (PE) on CT pulmonary angiograms (CTPAs) was performed by the University Hospital of Basel and presented at the European Congress of Radiology, showing that the Aidoc algorithm reached 93% sensitivity and 95% specificity.[23][24][25] Clinical research has also been performed to test the diagnostic performance of Aidoc's deep learning-based triage system for the flagging of acute findings in abdominal computed tomography (CT) examinations. Overall, the algorithm achieved 93% sensitivity (91/98, 7 false negatives) and 97% specificity (93/96, 3 false-positive) in the detection of acute abdominal findings.[26][27]
Additional clinical research on Aidoc's Intracranial hemorrhage algorithm accuracy was presented at the European Congress of Radiology by Antwerp University Hospital, evaluating the use of its deep learning algorithm for the detection of intracranial hemorrhage on non-contrast enhanced CT of the brain.[28] The University of Washington completed a study on the accuracy of Aidoc's intracranial hemorrhage algorithm.[29]
↑ Winkel, DJ; Heye, T; Weikert, TJ; Boll, DT; Stieltjes, B. (20 November 2019). "Evaluation of an AI-Based Detection Software for Acute Findings in Abdominal Computed Tomography Scans: Toward an Automated Work List Prioritization of Routine CT Examinations". Investigative Radiology. 54 (1): 55–59. doi:10.1097/RLI.0000000000000509. PMID30199417. S2CID52186362.
↑ Winkel, D. J.; Heye, T.; Weikert, T. J.; Boll, D. T.; Stieltjes, B. (20 November 2019). "Evaluation of an AI-Based Detection Software for Acute...: Investigative Radiology". Investigative Radiology. 54 (1): 55–59. doi:10.1097/RLI.0000000000000509. PMID30199417. S2CID52186362.
↑ P. Ojeda; M. Zawaideh; M. Mossa-Basha; D. Haynor (2019). "The utility of deep learning: evaluation of a convolutional neural network for detection of intracranial bleeds on non-contrast head computed tomography studies". In Angelini, Elsa D.; Landman, Bennett A. (eds.). Medical Imaging 2019: Image Processing. p.128. doi:10.1117/12.2513167. ISBN9781510625457. S2CID88494572.
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