The excitement over generative AI--and AI in general--has reached the multi-trillion-dollar healthcare industry, driven by the news of ChatGPT passing the United States Medical Licensing Exam (USMLE) and the rapid introduction of new healthcare-related AI applications. Bill Gates, for example, is recommending the use of generative AI tools for primary diagnoses of patients. While acknowledging that AI will inevitably misdiagnose patients, Gates argues that the upside is worth it. Preventing misdiagnoses that can impact, at the very least, a patient's quality of life, depends a lot on the quality and availability of the health data that is fed into the AI model. The current excitement notwithstanding, the development of AI healthcare solutions has been severely constrained by the dearth of comprehensive and representative real-world health data.

ChatGPT and similar systems will increasingly be part of our lives, including health care. We need guidelines to ensure their ethical deployment. Generative AI systems like ChatGPT, a chatbot based on a generative pre-trained transformer, have captured the public's attention, resulting in a flurry of positive and negative speculation about their potential. They have even found their way into popular comic strips. In one Dilbert strip, for instance, the boss asks Wally if his status report was written by a commercial grade AI.

For AI to flourish in healthcare, the industry must focus on the "algorithmically underserved," said John D. Halamka, M.D., M.S., president of Mayo Clinic Platform, at the HLTH 2022 conference this month in Las Vegas. Giving visibility to the algorithmically underserved -- individuals who do not generate enough data/are not well represented enough in health data sets for AI to make a determination -- is just one requirement to overcome the prospect of AI bias in healthcare. And identifying and fixing sources of AI bias must be a focus area for an industry that's striving for ethical and equitable AI development, shared Halamka. Dr. John Halamka is President of Mayo Clinic Platform, and a founding member of the Coalition for ... [ ] Health AI For example, what if there was a national registry that hosted all the metadata needed to power the responsible development of algorithms for use in healthcare? Building this kind of standardization into the relatively black box nature of AI development is among the priorities of The Coalition for Health AI (CHAI), which launched earlier this year.

Data used well via artificial intelligence and transparent algorithms offers health care a glimpse into democratization and equitable, efficient and efficacious care, according to an expert speaking at The Liver Meeting Digital Experience. "Over the next six quarters, ... we are going to see technology advancements, we are going to see policy and regulatory change and cultural expectations that will ask us to deliver cures in novel settings using novel methods and processes that will require us as providers to rethink how health care works in this country and internationally," John D. Halamka, MD, MS, president of the Mayo Clinic Platform, said during his President's Choice Lecture. Halamka challenged meeting attendees to adjust their views of what a platform is and what it offers to both physicians and patients. "Whether you're a provider or just a care navigator for a family ... in 2020 and 2021, care is often challenging to coordinate. It's not clear where you go next, what disease state you have, ... bringing the right patient to the right facility ... to get the right care ... is guesswork," he said.

John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article. The FDA's approach to software as a medical device (SaMD) has been evolving. In 2018, IDx-DR, a software system used to improve screening for retinopathy, a common complication of diabetes that affects the eye, became the first AI-based medical device to receive US Food and Drug Administration clearance to "detect greater than a mild level of … diabetic retinopathy in adults who have diabetes." To arrive at that decision, the agency not only reviewed data to establish its safety, it also took into account prospective studies, an essential form of evidence that clinicians look for when trying to decide if a device or product is worth using. The software was the first medical device approved by the FDA that does not require the services of a specialist to interpret the results, making it a useful tool for health care providers who may not normally be involved in eye care. The FDA clearance emphasized the fact that IDx-DR is a screening tool not a diagnostic tool, stating that patients with positive results should be referred to an eye care professional.

John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article. Asymptomatic left ventricular systolic dysfunction (ALVSD) may not be the most familiar disorder in medicine, but it nonetheless increases a patient's risk of heart failure and death. Unfortunately, ALVSD is not that easily detected. Characterized by low ejection fraction (EF) -- a measure of how much blood the heart pumps out during each contraction -- it's readily diagnosed with an echocardiogram. But because the procedure is expensive, it's not recommended as routine screening for the general public.

Editor's note: This article is based on a roundtable discussion sponsored by Optum. The full report of the roundtable discussion, Strategy: The Key Factor in the Future of Healthcare Innovation, is available as a free download. Innovation is paving the way for hospitals and healthcare systems to move into the future and truly change healthcare delivery. While solutions featuring artificial intelligence, robotic process automation, and natural language processing are fueling advances, healthcare innovation is more than a technology play. Effective transformation requires formulating new strategies for payment, reimagining models of care, applying real-time data, and addressing social determinants of health.