This is the second part of a four-part series, examining the ways in which we can architect “The AI Physician” and the pieces involved in bringing this idea to fruition. As the pairing of man and machine has proven to result in noteworthy positive clinical impacts, what steps ought to be taken to educate physicians about AI for the purpose of improving patient outcomes in the next decade? Here, we dive into the role of The Innovative Radiologist:
Of all specialties within medicine, radiology is uniquely situated at the vertex of clinical medicine and technology. Historically, the reading room invites physicians to think deeply about the symbiotic relationship between medicine and technology. Although radiologists currently face an enormous burden of reading more scans with less time, the heart of the field of radiology is innovation. Newer, better, shinier and cooler tools within the diagnostic and interventional side of radiology are embraced by the field, almost opening the door for other specialties within the hospital to eventually adapt accordingly. It is not only the nature of the field that invites constant innovation, but the culture of radiology supports an environment of thinking futuristically towards what medicine could look like in a decade from now.
The role of the radiologist is being augmented – and perhaps transformed – through AI. From image acquisition to interpretation and reporting, AI impacts every aspect of the modern day radiologist’s workflow.
Focusing on a few limited use cases of artificial intelligence can help guide the education of the future AI physician:
The field of radiology has graduated from the chapter of feeling threatened by AI to a unified understanding that radiologists must own the deployment of AI in their field. In order to do so, radiologists require a structured curriculum to teach them the basics of AI. An international survey of radiology residents revealed that the majority agreed that AI ought to be taught in residency, with many feeling that AI education was inadequate. By training radiologists in AI, they can learn to collaborate with AI algorithms, enabling them to identify subtle abnormalities, detect early signs of diseases, and reduce the risk of misdiagnosis. The combination of physician expertise and AI-driven insights can lead to more precise diagnostic capabilities, improved workflow efficiencies and better patient management.
Radiology is inherently a data rich and technologically supportive field of clinical medicine. Nevertheless, a structured methodology by which radiology residents and attending physicians can become more familiar with AI algorithms would alleviate some of the pressures associated with learning yet another field of expertise. The goal of the “innovative radiologist” is not to become an artificial intelligence engineer or a computer scientist. There is an enormous learning curve in the field of radiology for newly minted physicians and early developing radiologists.
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