by
John R. Fischer, Senior Reporter | November 21, 2022
From the November 2022 issue of HealthCare Business News magazine
The FDA, to date, has cleared over 200 AI applications for various uses in radiology, says Dr. Bibb Allen, FACR, chief medical officer for the American College of Radiology’s Data Science Institute and a general diagnostic radiologist in the Birmingham Radiological Group at Grandview Medical Center in Alabama. He warns that FDA clearance does not equate to universal efficacy.
“We just can’t rely on an FDA clearance to say, ‘Right, it’s FDA-cleared, so that means it can be used on every patient based on every example we have.’ That’s clearly not the case,” he said.
Bias incorporated in the development and training process can also interfere with AI’s clinical accuracy when deployed. “If there are racial differences or gender differences, and we haven’t properly characterized that because we don’t have enough of the various subpopulations, then we’re at risk for not providing optimal care for our patients,” said Dr. Bradley Erickson, professor of radiology and director of the Mayo AI lab.
Erickson says that larger, higher-quality data curated by standards is necessary for eliminating biases. In agreement is Dr. James Brink, enterprise chief of radiology for Mass General Brigham Health System, radiologist in chief at Mass General Hospital, and chair of radiology at Brigham and Women’s Hospital, who says that the best way to go about this is to train and develop AI sequentially in various facilities with diverse demographics, populations, data environments, and different equipment and systems.
“By circulating algorithms in development among multiple sites, a federated approach to algorithm development can help eliminate the biases that would come with simply training in one environment with one demographic,” he said.
Dr. Shandong Wu, a tenured associate professor of radiology and founding director of the Pittsburgh Center for AI Innovation in Medical Imaging, adds that more clinical trials are needed for AI evaluation, with only a handful currently underway worldwide.
He says that radiologists must be involved and perform rigorous assessments that result in evidence that not only shows how a specific model works but also helps understand why it is capable of performing a task.
“If the model can do some explainable diagnosis using radiologist-acquainted clues, that is helpful for physicians to understand how it works and to gain their trust and perspective,” he said.
Overcoming roadblocks
With inflation through the roof and tighter margins, radiologists, like other clinicians, are concerned about their return on investment when it comes to the technologies they use and procedures they perform. This has been a major roadblock for the adoption of AI, with no standardized reimbursement system set up to compensate clinicians utilizing the technology.
