Generative AI in Healthcare: Promise and Skepticism
Generative AI is capable of creating and analyzing images, text, audio, videos, and more. It is gradually making its way into healthcare, driven by both Big Tech firms and startups. Google Cloud is Google’s cloud services and products division. It is working together with Highmark Health on generative AI tools intended to personalize the patient intake experience. It is a Pittsburgh-based nonprofit healthcare company. Amazon’s AWS division is working with unnamed customers on a way to use generative AI to evaluate medical databases for “social determinants of health”. Microsoft Azure is helping to build a generative AI system for to automatically prioritize messages to care providers sent from the patients. Providence is a not-for-profit healthcare network.
Prominent generative AI startups in healthcare include Ambience Healthcare, which is developing a generative AI app for clinicians; Nabla, which is an ambient AI assistant for practitioners; and Abridge, which creates analytics tools for medical documentation. The broad eagerness for generative AI is reflected in the investments in generative AI efforts aimed at healthcare. Mutually, generative AI in healthcare startups has raised tens of millions of dollars in project capital to date, and the vast majority of health investors say that generative AI has meaningfully influenced their investment strategies.
Still, both professionals and patients are mixed as to whether healthcare-focused generative AI is ready for prime time. In a recent Deloitte survey, only about half (53%) of U.S. consumers said that they thought generative AI could progress healthcare. Fewer than half said they expected generative AI to make medical care more affordable. Andrew Borkowski is a chief AI officer at the VA Sunshine Healthcare Network, the U.S. Department of Veterans Affairs’ largest health system. He doesn’t think that the suspicion is unjustified. Borkowski warned that generative AI’s disposition could be premature due to its “significant” limitations.
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