The AI market is full of grand promises for transformation, and healthcare is a prime target, beleaguered by financial pressures, labor shortages, and the growing burden of caring for an aging population. AI developers are targeting functions that vary widely, from curing cancer and performing surgery to streamlining routine administrative tasks.
While the opportunity is genuine, execution can be difficult. Numerous software vendors have tried to “fix” healthcare challenges but failed due to a misunderstanding of the environment. Steve Bethke, vice president of the solution developer market for Mayo Clinic Platform, states, “Healthcare is very complex. Solution developers must have a deep focus on clinical and technical capabilities, and then align their solutions to the relevant business impacts. If they miss any dimension, the solution will not be adopted or drive value.”
AI applications for healthcare are proliferating rapidly. The U.S. Food and Drug Administration (FDA) has approved more than 1,300 AI-enabled medical devices, mostly for interpreting diagnostic images. Over half of these were approved in the past three years, with the earliest dating back to 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 handling scheduling and administrative tasks—are more difficult to track but are also rapidly increasing. AI can help coordinate complex tasks and workflows 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% prioritized AI for reducing caregiver burden and improving caregiver satisfaction, while over half (53%) cited workflow efficiency and productivity.
Any healthcare-related application can potentially impact patient care, directly or indirectly. Poorly designed or inadequately trained and validated AI apps can put patients at risk. Providers recognize this 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 healthcare observes.
To tackle some of the technical challenges, many healthcare providers are partnering with application developers to build AI solutions. In a recent study, McKinsey found that 61% of healthcare 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.