Today, plenty of hope, fear, and hype surround the use of artificial intelligence (AI) in radiology. With media attention and many startups focused on using AI to identify findings within medical images, it’s easy for us in the radiology profession to have tunnel vision about AI in our field. However, it’s important to widen our aperture to see the many other ways AI can benefit medical imaging.
I recently presented at the International Society for Strategic Studies in Radiology’s biennial symposium about what the radiologist's job will look like in 2025. During my presentation, I referenced a figure of the imaging value chain from an article in the Journal of the American College of Radiology1 to illustrate how AI will augment and improve all components of the chain during the next eight years.
For starters, clinical decision support will help referring clinicians make better-informed decisions when ordering imaging exams. From there, AI will automate the steps within the imaging value chain, including patient scheduling, protocol optimization, modality operations, image interpretation, actionable report creation, communication of findings to referring clinicians and patients, quality assessment, and patient safety and follow-up.
AI will handle mundane tasks like measuring lymph nodes and conducting comparisons — saving us time and allowing us to move toward more quantitative imaging in which we will generate higher-quality reports with fewer errors and improved differentials and diagnoses.
In the era of value-based imaging, AI will also enable us to increase our interactions with patients. We will have more time to spend explaining, counseling, and teaching patients about our role in their care and about their imaging results. Further, it will afford us time to explore new avenues for enhanced patient care.
Throughout history, radiologists have adapted to technological innovations and made adjustments to integrate them into our workflow. AI will soon become an integral part of our lives and our profession. As we learn to integrate AI into radiology, we must have a wider field-of-view beyond interpretation to take full advantage of this emerging technology and maximize our role in patient care.
Boland GW, Duszak R Jr, McGinty G, Allen B Jr.Delivery of appropriateness, quality, safety, efficiency and patient satisfaction. J Am Coll Radiol. 2014;11(1):7-11.
By Woojin Kim, MD, chief medical information officer in the Healthcare Division of Nuance Communications