Woven Care Intelligence

Woven care intelligence is a transformative approach to clinical AI that weaves together patient data, clinical care teams and AI tools to dramatically improve patient care and facility outcomes.

A Solution to Fragmentation 
in Healthcare

With siloed information in a complex system, the threads in your hospital are disconnected. While different AI tools have shown promise, they’ve ultimately offered disjointed experiences to users and patients, feeding further fragmentation — fragmentation that can have deadly consequences.

Woven care intelligence is here to change that. As the architect that weaves disparate threads together, woven care intelligence is an approach that creates a smarter, more connected healthcare system and better outcomes for patients.

The Definitive Guide to Woven Care Intelligence

Many AI tools have shown promise, but in fragmented hospital systems, they create more silos. Find out how woven care intelligence weaves these disparate threads together to create a unified approach to AI.

A Transformative New Approach to Clinical AI

Woven care intelligence is a dramatic shift in the way hospitals leverage the power of clinical AI. Here’s how it compares to the outgoing paradigm.

Pioneering an Approach 
to Woven Care Intelligence

Aidoc set the standard for how AI should be scaled in healthcare with the aiOS™, the first woven care intelligence platform for enterprise AI adoption. The aiOS™ helps health systems reap full benefits of transformative AI technology.

Woven care intelligence impact


7 hr.

in time from imaging to thrombectomy with AI at Cedars Sinai Medical Center1



readmission reduction rate at 13 sites using Woven Care Intelligence2

day reduction


inpatient length of stay 
for intracranial hemorrhage patients at Yale New Haven Health3



over a five-year period driven by improved patient care, increased appropriate interventions and improved productivity4

Talk with a woven care intelligence expert

  1. Luh, J. Y., Thompson, R. C., & Lin, S. H. (2019). Clinical Documentation and Patient Care Using Artificial Intelligence in Radiation Oncology. Journal of the American College of Radiology. https://doi.org/10.1016/j.jacr.2019.05.044
  2. Davis, M. J., Rao, B. M., Cedeño, P. A., Saha, A., & Zohrabian, V. M. (2020). Machine Learning and Improved Quality Metrics in Acute Intracranial Hemorrhage by Noncontrast Computed Tomography. Current Problems in Diagnostic Radiology, 51(4), 556–561. https://doi.org/10.1067/j.cpradiol.2020.10.00
  3. Aidoc. (2023). A Medicare Claims-Based Analysis Of The Impact Of AI On Readmission Rates. [White paper]
  4. Bharadwaj P, Nicola L, Breau-Brunel M, Sensini F, Tanova-Yotova N, Atanasov P, Lobig F, Blankenburg M. Unlocking the Value: Quantifying the ROI of Hospital AI. J Am Coll Radiol. 2024 Mar 16:S1546-1440(24)00292-8. doi: 10.1016/j.jacr.2024.02.034. Epub ahead of print. PMID: 38499053