Business View Magazine l October 2022

92 BUSINESS VIEW MAGAZINE VOLUME 9, ISSUE 10 of machine learning algorithms designed to enhance the decision-making of clinicians. Some of the most common examples are devices that harness image recognition (driven by machine learning) to assists in the detection of illness. The program known as GI Genius, for instance, became the first AI-based device to be granted premarket authorization by the U.S. FDA for detecting colon cancer. There are also now programs that autonomously flag signs of diabetic retinopathy during scans of a patient’s retina. “And healthcare professionals who are not trained in ophthalmology at all carry this scan out,” added Sparnon. “That’s a big deal!” In this way, the technology is not replacing the human element, but instead is enhancing the capabilities of humans so they better interact with their patients. “The way I see it, AI won’t replace physicians, but physicians who use AI will replace those who don’t,” Ehrenfeld said. “It’s increasingly likely that AI is going to be an essential tool that helps augment our practice, ensures higher-quality care, and brings additional value to the healthcare system.” AI, the Ineffable What complicates an otherwise bright future is the seemingly unknowable nature of an AI’s machine learning algorithm. “One of the interesting aspects of this technology is that it can continue to learn and improve itself over time,” Baird explained. “A system might be initially trained on a fixed set of images, but as it is used in multiple hospitals over a period of time, more training data can be collected and fed back into the learning system. Just as we gain experience over time, so can this software.” This poses a unique challenge for regulators, who cannot reasonably predict how a machine learning algorithm might evolve in a unique healthcare environment. How do you ensure the safety and efficacy of a program that can change itself after premarket approval?