On Thursday, the U.S. Department of Health and Human Services announced new funding to help improve the use and maintenance of artificial intelligence-enabled medical devices.

Research has shown that machine learning models used in clinical settings may degrade over time. The new HHS money is earmarked for the Advanced Research Projects Agency for Health, or ARPA-H, which will use it to innovate its efforts to make AI tools more reliable for doctors and more beneficial for patients.

That initiative is known as the Performance and Reliability Evaluation for Continuous Modifications and Useability of Artificial Intelligence, or PRECISE-AI.

WHY IT MATTERS

More than 950 medical devices with integrated AI functionalities have been authorized by the U.S. Food and Drug Administration – representing a tenfold increase from 2018, according to ARPA-H.

While these new AI-enabled tools may transform doctors’ ability to provide care, ML may degrade over time due to changes in input data from clinical operations, data acquisition, patient population or IT infrastructure. 

"The promise of AI-enabled tools for healthcare is only as strong as the relevant real-world data informing them," Berkman Sahiner, PRECISE-AI program manager, said in a statement. 

Current techniques challenge users to monitor and maintain AI-driven device performance in real-world clinical settings.

"The inability to automatically monitor and maintain an AI-enabled medical device’s performance based on real-world operations creates risks for clinicians and patients," Sahiner added.

Through PRECISE-AI, HHS said it seeks to establish an open-source repository of tools that maintain AI-enabled clinical decision support by automatically identifying and correcting performance degradation – without the need for human oversight. 

The root-cause analysis tools also need to communicate clear and actionable information about the sources of degradation to enable healthcare users to better interpret model uncertainty. 

By "recommending the optimal approach to detect and mitigate the underlying AI model performance degradation," clinicians can provide better patient care, said Sahiner.

The forthcoming final program solicitation will focus on five technical areas, and teams will start by developing tools to establish the most accurate estimate of a patient’s diagnosis, given the available evidence, or "ground truth," the agency said. 

The teams will then develop autonomous methods to monitor an AI tool’s performance against these ground truths, determine the root causes of any degradation and make necessary corrections. 

They'll also create mechanisms and the underlying data infrastructure for notifying clinicians, AI tool developers, hospital administrators and regulators when performance degradation occurs. 

The response date for the solicitation is currently January 15, 2025. 

THE LARGER TREND

As an independent entity of the National Institutes of Health, ARPA-H seeks to invest in ways to build stronger, healthier and more resilient healthcare systems.

It focuses on creating scalable platforms and strategies from advanced mobile hospitals that deliver acute care in rural settings to securing open-source software used in critical infrastructure.

As part of its Digital Health Security Initiative, DIGIHEALS, ARPA-H partnered with the Defense Advanced Research Projects Agency on the Artificial Intelligence Cyber Challenge. In May, HHS announced $50 million under ARPA-H to help providers patch ransomware vulnerabilities across networks and medical devices.

ON THE RECORD

"PRECISE-AI is addressing a growing gap in ensuring AI tools used in clinical decision-making are accurate, safe, and robust in real-world settings," said ARPA-H Director Renee Wegrzyn in the solicitation announcement. "In doing so, ARPA-H is creating a foundation of trust between clinicians and these AI tools, which will further expand AI’s potential in improving health outcomes for all Americans."

Andrea Fox is senior editor of Healthcare IT News.
Email: afox@himss.org
Healthcare IT News is a HIMSS Media publication.

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