Technology is changing the face of medicine. Although cutting-edge research in such areas as artificial intelligence (AI) has not yet produced concrete applications specific to rheumatology, savvy practitioners are already envisioning the day it will.
This group includes Suleman Bhana, MD, a rheumatologist and self-professed techie, who loves his Tesla Model S. He especially enjoys the autopilot mode, which makes the long commute to his offices easier to endure. On a recent drive, while listening to an audio version of an article about advances in bedside computer vision, the wheels in Dr. Bhana’s head started turning. If Tesla can build a semi-autonomous vehicle and researchers have shown AI can effectively classify skin-lesion images and interpret radiology scans (see below)—what AI opportunities are ahead for rheumatologists?
Right now, the question is unanswerable, but rheumatologists would do well to be ready for advances not yet thought of, says Dr. Bhana, who practices with Crystal Run Healthcare, Middletown, N.Y.
“Technology is all around us,” he says. “It’s a matter of integration, testing and getting it working. I think there are many inefficiencies in our system that we can undo with the proper use of technology.”
The NEJM article Dr. Bhana was listening to, “Bedside Computer Vision – Moving Artificial Intelligence from Driver Assistance to Patient Safety,” focused on the progress made in medical applications of AI during the past five years.1 Lead author Serena Yeung, PhD, a recent graduate of the Department of Computer Science and the Clinical Excellence Research Center at Stanford University, and her colleagues reported that AI was equal to 21 board-certified dermatologists in classifying digital images of benign and malignant skin lesions.2 She also points to smaller studies that demonstrated progress interpreting radiologic and pathological images, as well as machine interpretation of video data of clinician behaviors in operating rooms and ICUs, noting that AI is “poised to gain a foothold in screening medical images” and quantifying “progress in patients’ mobility.”3,4
“Right now, the bottleneck is really our understanding of all the applications AI can be used for,” Dr. Yeung says, noting algorithms are cost efficient and easy to maintain. “AI has huge potential, not only [for] disease diagnosis and treatment selection, but also in improving the day-to-day execution of care. This means once a treatment is decided, AI can assist with ensuring intended steps [are taken] in care, and [in] executing and improving personalized patient care plans.”