Will AI ultimately replace or assist radiologists?

por John R. Fischer, Senior Reporter | November 21, 2022
Artificial Intelligence X-Ray
From the November 2022 issue of HealthCare Business News magazine

From scheduling patients to interpreting images, artificial intelligence is poised to fundamentally change radiology, offering new potential applications and insights that will enhance scanning accuracy, workflow and patient outcomes.

But it’s still early, practices have been slow to adopt the technology and there are more questions than answers. From concerns over accuracy and biased algorithms, to skepticism over the specific value AI can provide on a day-to-day basis. From a wide-angle perspective, some healthcare practitioners, including medical students, worry the technology could someday render radiologists obsolete.

For Dr. Michael Atalay, professor in the department of diagnostic imaging and vice chair of imaging research in the Alpert Medical School at Brown University, addressing concerns about AI someday replacing radiologists is an important part of the job. He stresses, to students and colleagues alike, that AI will “assist” radiologists.
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“We have to show how we’re embracing the new technology and the firm belief that it will enhance our roles as diagnosticians and improve patient safety and outcomes,” Atalay, who also serves as director of cardiac MR and CT at the Alpert Medical School, told HCB News. “Along the way it will also likely improve job satisfaction and quality of life and decrease the high physician burnout rates that we’re seeing now.”

Figuring out where and how AI fits into an individual radiology practice’s workflow, Atalay and others say, will be a critical step toward gaining physician trust and accelerating adoption.

Gaining the radiologist’s trust
Workflow at one practice is unlike another, based on different demographics, patient populations, data environments, prevalence of conditions and several other factors. As a result, AI algorithms that work well in one place may do little for or even hamper another’s workflow.

For instance, for a period, Rhode Island Hospital’s radiology department adopted an application for identifying intracranial hemorrhage on CT scans in the emergency department. While potentially beneficial in other facilities, the technology created delays in the busy ED’s workflow and was eventually removed.

For AI to be useful, providers must also understand how to use it in their specific workflow, according to Atalay. “Does the AI live at the scanner, on the PACS station, or on a standalone workstation? Does every pertinent imaging study have to go to that site or through an application somewhere else before it is eventually reviewed?” he said. “To improve efficiency and efficacy—and not cause disruption—these tools should be directly and transparently integrated into the workflow.”

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