AI applications for health care are proliferating rapidly. The U.S. Food and Drug Administration has approved more than 1,300 AI-enabled medical devices, mostly for interpreting diagnostic images. More than half of these were approved in the past three years, with the earliest dating as far back as 1995. Non-radiological applications carry out tasks as diverse as tracking sleep apnea, analyzing heart rhythms, and planning orthopedic surgeries.
AI applications that do not count as medical devices— for example, those that handle scheduling and administrative tasks—are more difficult to track but are also rapidly increasing. AI can help coordinate complex tasks and workflows that are often conventionally managed by whiteboards and sticky notes. Such functions may well outstrip clinical uses in their impact on health systems. A recent survey of technology leaders found that 72% said their top priority for AI was reducing caregiver burden and improving caregiver satisfaction, while over half (53%) cited workflow efficiency and productivity.
Any health care-related application can potentially impact patient care, whether directly or indirectly, and AI apps that are poorly designed or inadequately trained and validated can put patients at risk. Providers recognize that risk: In the same survey, 77% said immature AI tools are a significant barrier to adoption. Regulators and lawmakers are also keeping an eye on the risks as development and adoption burgeon, though the U.S. regulatory picture is still in flux, as a 2024 report to Congress on AI in health care observes.
To tackle some of the technical challenges, many health care providers are partnering with application developers to build AI solutions. In a recent study, McKinsey found that 61% of health care organizations intend to pursue partnerships with third-party vendors to develop customized generative AI solutions as a primary strategy as opposed to building them in-house or buying off-the-shelf products.
But health care-specific AI applications must also be tailored to the nuanced clinical needs of medical providers as well as the complex business and regulatory considerations of the wider sector. This is where developers can benefit from working with a partner with a deep understanding of the health care environment to tailor applications to what providers want and need most. Doing so helps to position AI products for maximum impact and value, avoiding the pitfalls unique to the health care environment.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
Stephan is the sports journalist for the Maple Grove Report.
