Ariella Shoham

Aidoc Kicks Off RSNA Showing Why Radiologists Lead the Way in Healthcare AI

Aidoc’s AI-driven care solutions focus on connecting all points of care through new functionality and expanded solutions showcased at RSNA

Aidoc, the leading provider of healthcare AI solutions, enters the Radiological Society of North America’s (RSNA) 2022 annual meeting with a series of recent clearances, accolades and investments demonstrating the continued rapid adoption—and massive potential—of artificial intelligence.

“From new funding rounds to expanding our solutions to cover multiple service lines, Aidoc has experienced numerous milestones since our time at RSNA last year,” said Elad Walach, co-founder and CEO, Aidoc. “Yet, we remain steadfastly focused on where we started – with radiology. We believe radiology practices are the AI champions and early adopters within health systems., As the most established healthcare AI users, these physicians can help guide hospital leadership to invest in AI solutions to further improve patient and financial outcomes.” 

Accolades and Enhancements for aiOS™
Since its founding in 2016, Aidoc has revolutionized how health systems deliver a connected care experience that delivers critical information when and where care teams need it, leading to immediate collective action.

What started with image-based AI has quickly expanded to further connect and maximize the care experience through Aidoc’s proprietary aiOS™, a first-of-its-kind operating system that enables organizations to reliably deploy AI solutions in high volumes and overcome the challenges associated with legacy IT systems and separate physician workflows. Named “Best New Radiology Software” at the prestigious 2022 AuntMinnie.com awards, Aidoc is further enhancing aiOS’ patient management offering by enabling an always on, automated end-to-end platform for patient care and coordination.

Additionally, Aidoc is now available in the Epic App Marketplace, enabling efficient integration with Epic’s Electronic Health Records (EHR) platform. By integrating with Epic, Aidoc’s aiOS streamlines clinical workflows and automatically provides physicians with real-time clinical information and actionable AI insights on patient care.

Introducing New Patient Management Functionality
Aidoc is introducing enhancements to its patient management offering by enabling an always on, automated end-to-end platform for follow-up management and care coordination.

An estimated 12 percent of patient cases recommended some type of follow-up1. The challenge is that health systems often lack a closed-loop communication system, encumbering ordering clinicians and teams with the onerous task of manually connecting all the dots through disparate systems that don’t communicate effectively. This could potentially add hours of more work to an already overburdened system and risk patients falling through the cracks.

Aidoc’s patient management solution removes those system barriers by creating an end-to-end process automating the recommendation of potential follow-up actions with deep integration into the EHR, resulting in closed-loop patient care. “Our newest offering enables teams to not only manage needs in active cases but also draw attention to potential unexpected findings that may need timely care and management,” stated Demetri Giannikopoulos, vice president of innovation, Aidoc. “We make follow-up easy, connecting the dots along the patient’s pathway of care.”

Clinical Successes
Aidoc’s software is now being used in more than 1,000 hospitals worldwide, and its impact in the industry has been undeniable. A recent study conducted by Cedars-Sinai Medical Center found that implementing Aidoc’s AI-augmented radiological worklist triage system helped decrease the length of hospital stays for patients diagnosed with intracranial hemorrhage (ICH) and pulmonary embolism (PE) by 1.3 days and 2.07 days, respectively.

The impact of Aidoc’s technology is also apparent in the 16 abstracts being showcased at RSNA, including a retrospective study presented by Envision Healthcare exploring Effectiveness of a Convolutional Neural Network Artificial Intelligence Algorithm in the Detection of Intracranial Hemorrhage on Noncontrast CT Imaging. In the retrospective study, Envision Healthcare looked at 8,468 CTs from 29 different facilities and demonstrated robust sensitivity/specificity (94%/99%) along with a potential enhanced triage rate of 5.8 percent.

FDA Clearances
Aidoc has increased its FDA clearances to 12 with the addition of two CT-based AI solutions – aortic dissection (AD) and all vessel occlusions (VOs) covering the various LVOs and MeVOs. These additions continue Aidoc’s impressive momentum and leadership in healthcare AI, giving the organization the most FDA clearances in the AI imaging space. 

Funding Expansion
The above successes have also attracted additional investment. Aidoc recently raised $110 million through a Series D funding round led by investors such as Technology Crossover Ventures and Alpha Intelligence Capital.

Aidoc will have an on-site booth in McCormick Place during the RSNA Annual Meeting where attendees can demo the end-to-end radiology experience and see firsthand how the exclusive aiOS functions as an always on extension of hospital health systems that enable facilities to build an intelligent and connected health enterprise.

About Aidoc

Aidoc is the leading provider of artificial intelligence healthcare solutions that empower physicians to expedite patient treatment and enhance quality of care. Aidoc’s AI-driven solutions analyze medical images directly after the patient is scanned, suggesting prioritization of time-sensitive pathologies, as well as notifying and activating multidisciplinary teams to reduce turnaround time, shorten length of stay, and improve overall patient outcomes.

1. Cochon, L. R., Kapoor, N., Carrodeguas, E., Ip, I. K., Lacson, R., Boland, G., & Khorasani, R. (2019). Variation in Follow-up Imaging Recommendations in Radiology Reports: Patient, Modality, and Radiologist Predictors. Radiology, 291(3), 700–707. https://doi.org/10.1148/radiol.2019182826

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Ariella Shoham