They will also test whether the model or the data were vulnerable to intrusion at any point.įuture phases will utilize HIPAA-protected data within the context of a federated environment, enabling algorithm developers and researchers to conduct multi-site validations. A clinical grade algorithm which rapidly identifies those needing blood transfusion in the Emergency Room following trauma will be used as a reference standard to compare validation results. The organizations will leverage the confidential computing capabilities of Fortanix Confidential Computing Enclave Manager, Intel’s Software Guard Extensions (SGX) hardware-based security capabilities, Microsoft Azure’s confidential computing infrastructure, and UCSF’s BeeKeeperAI privacy preserving analytics to calibrate a proven clinical algorithm against a simulated data set. With this new technology, we expect to markedly reduce the time, and cost while also addressing data security concerns.” “Much of the cost and expense was driven by the data acquisition, preparation, and annotation activities. “While we have been very successful in creating clinical grade AI algorithms that can safely operate at the point of care, such as immediately identifying life threatening conditions on X-rays, the work was time consuming and expensive,” said Michael Blum, MD, associate vice chancellor for informatics, executive director of CDHI and Professor of Medicine at UCSF. Few research groups, or even large healthcare organizations, have access to enough high-quality data to accomplish these goals. ![]() Algorithms that are used in the context of delivering healthcare must be capable of consistently performing across diverse patient populations, socioeconomic groups, geographic locations, and be equipment agnostic. The platform will provide a “zero-trust” environment to protect both the intellectual property of an algorithm and the privacy of healthcare data, while CDHI’s proprietary BeeKeeperAI will provide the workflows to enable more efficient data access, transformation, and orchestration.ĭid you know that just validation of an algorithm takes 16-30 months, $1.5M – $2M total cost? Gaining regulatory approval for clinical artificial intelligence (AI) algorithms requires highly diverse and detailed clinical data to develop, optimize, and validate unbiased algorithm models. UC San Francisco’s Center for Digital Health Innovation (CDHI), Fortanix, Intel, and Microsoft Azure have formed a collaboration to establish a confidential computing platform with privacy-preserving analytics to accelerate the development and validation of clinical algorithms. ![]() ![]() – This will help get life-saving clinical applications toĪll patients faster, such as predicting the need for blood transfusions orĭetecting tumors on X-rays, while maintaining patient privacy using – UCSF, in partnership with Fortanix, Intel, and Microsoft, will announce BeeKeeperAI, a Privacy-Preserving Analytics Platform that will accelerate the development of clinical AI algorithms by 1000X (from 30 months to 1 day).
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