FDA Offers Vote of Confidence for AI in Healthcare

The FDA, in turn, is developing improved strategies for regulation and launching new projects at the FDA's internal data science incubator.

The Information Exchange and Data Transformation (INFORMED) incubator will explore opportunities for machine learning and AI to improve existing clinical practices, such as open-access platforms and shared, continually reconciled database exchange technologies such as blockchain, which can enable the widespread and secure exchange of health data. Among the ongoing projects is collaboration with Project Data Sphere, a nonprofit open-access cancer data repository, aimed at developing algorithms for classification of tumor dynamics using medical imaging data.

“We’re implementing a new approach to the review of artificial intelligence. FDA's regulatory approach will focus on the ways in which real-world data flows. This includes structured and unstructured data from pathology slides, electronic medical records, wearable devices, and insurance claims data,” said Dr. Gottlieb. “We want to better understand, and unlock ways, that this data can be used to inform development and validation of AI devices.”

“FDA also plans to ensure its regulatory framework and software validation tools are sufficiently flexible to keep pace with the unique attributes of this rapidly advancing field. That said, the agency still needs to establish appropriate guardrails for patients,” concluded Dr. Gottlieb. “It’ll look at developing new clinical endpoints and signal detection methods for evaluation of the safety and effectiveness of therapies. These efforts also will help us develop new approaches for understanding variations in individual patient experience using diverse data sets from clinical trials, EHRs, and biometric monitoring devices.”

The FDA has also recently launched a fellowship program with Harvard University (Boston, MA, USA) on AI and machine learning, which is focused on designing, developing, and implementing algorithms for regulatory science applications. One such example is innovative clinical decision support software that uses AI algorithms to help alert neurovascular specialists of brain deterioration.

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