Federal regulators have cleared dozens of AI products used in health care, which might give the impression that the Food and Drug Administration has a firm handle on a technology that is already changing how patients are treated. But a meeting on AI regulation last week told a different story. The agency is still grappling with fundamental questions about algorithmic bias, data transparency, and how to ensure that patients benefit equally from AI's rapid progress in medicine. Unlock this article by subscribing to STAT and enjoy your first 30 days free! STAT is STAT's premium subscription service for in-depth biotech, pharma, policy, and life science coverage and analysis. Our award-winning team covers news on Wall Street, policy developments in Washington, early science breakthroughs and clinical trial results, and health care disruption in Silicon Valley and beyond.
At a virtual meeting of the U.S. Food and Drug Administration's Center for Devices and Radiological Health and Patient Engagement Advisory Committee on Thursday, regulators offered updates and new discussion around medical devices and decision support powered by artificial intelligence. One of the topics on the agenda was how to strike a balance between safety and innovation with algorithms getting smarter and better trained by the day. In his discussion of AI and machine learning validation, Bakul Patel, director of the FDA's recently-launched Digital Health Center of Excellence, said he sees huge breakthroughs on the horizon. "This new technology is going to help us get to a different place and a better place," said Patel. You're seeing automated image diagnostics.
While AI and machine learning have the potential for transforming healthcare, the technology has inherent biases that could negatively impact patient care, senior FDA officials and Philips' head of global software standards said at the meeting. Bakul Patel, director of FDA's new Digital Health Center of Excellence, acknowledged significant challenges to AI/ML adoption including bias and the lack of large, high-quality and well-curated datasets. "There are some constraints because of just location or the amount of information available and the cleanliness of the data might drive inherent bias. We don't want to set up a system and we would not want to figure out after the product is out in the market that it is missing a certain type of population or demographic or other other aspects that we would have accidentally not realized," Patel said. Pat Baird, Philips' head of global software standards, warned without proper context there will be "improper use" of AI/ML-based devices that provide "incorrect conclusions" provided as part of clinical decision support.
Sinai School of Medicine, Stanford University and the Northern California Institute for Research and Education, IBM Research is undertaking a new research initiative funded by the National Institutes of Health. As part of a broader $99 million, 5-year research initiative spanning multiple public and private organizations and research institutions, this work will tap into AI and big data to help better identify individuals at high-risk of developing schizophrenia, a serious mental illness affecting how a person thinks, feels and behaves. Schizophrenia is often characterized by alterations to a person's thoughts, feelings and behaviors, which can include a loss of contact with reality known as psychosis. A better understanding of how this disease could be detected prior to psychosis could help to postpone or even prevent the transition to psychosis, as well as possibly improve outcomes. The project is a component of the Accelerating Medicines Partnership (AMP), a collaboration between the National Institutes of Health (NIH), the U.S. Food and Drug Administration (FDA), pharmaceutical companies, biotech firms and nonprofit organizations.
Scopio Labs, a leading provider of Full Field Morphology (FFM), announced that it was granted FDA clearance to market and sell its X100 with Full Field Peripheral Blood Smear (Full Field PBS) Application, unlocking the potential of in vitro hematology diagnosis. Full Field PBS is also available in Europe with CE mark certification granted earlier this year. Blood is one of the most foundational gateways to health information. Even with the adoption of digital tools, today's solutions do not showcase all required regions of interest in a PBS slide, only capturing snapshots of cells. To help improve diagnostic accuracy leveraging novel computer vision tools, Full Field PBS gives clinical laboratories an unprecedented ability to capture digital scans using advanced computational photography imaging and tailored AI tools.
The U.S. Food and Drug Administration on Thursday convened a public meeting of its Patient Engagement Advisory Committee to discuss issues regarding artificial intelligence and machine learning in medical devices. "Devices using AI and ML technology will transform healthcare delivery by increasing efficiency in key processes in the treatment of patients," said Dr. Paul Conway, PEAC chair and chair of policy and global affairs of the American Association of Kidney Patients. As Conway and others noted during the panel, AI and ML systems may have algorithmic biases and lack transparency – potentially leading, in turn, to an undermining of patient trust in devices. Medical device innovation has already ramped up in response to the COVID-19 crisis, with Center for Devices and Radiological Health Director Dr. Jeff Shuren noting that 562 medical devices have already been granted emergency use authorization by the FDA. It's imperative, said Shuren, that patients' needs be considered as part of the creation process.
Aidoc announced today that the US Food and Drug Administration (FDA) has given regulatory clearance for the commercial use of its triaging and notification algorithms for flagging and communicating incidental pulmonary embolism . Flagging incidental, critical findings is a huge technical challenge due to the varied imaging protocols used and lower incidences of such cases. The ability to prioritize incidental critical conditions accurately is a breakthrough in the value AI can bring to the radiologist workflow. "The most common use case we experienced is for critical unsuspected findings in oncology surveillance patients" said Dr. Cindy Kallman, Chief, Section of CT at Cedars-Sinai Medical Center. "The ability to call the referring physician while the patient is still in the house is huge. We are essentially offering a point-of-care diagnosis of PE for our outpatients. Our referring physicians have been completely wowed by this."
For quite a while, artificial intelligence and machine learning models are leveraged in the healthcare industry to improve patient outcomes. They have been utilized in various scans, for diagnosing various diseases, for the drug manufacturing and planning the treatment for various diseases. The involvement of these AI/ML models is observed in the surgical process as well. With the amount of data being generated nowadays, the traditional AI/M- based software models are often scrutinized under the lens of performance and accuracy. As new advances are shaping the future of healthcare, the modification of the existing software models has been recognized by healthcare professionals.