Custom AI partnerships reshape health care strategy as off-the-shelf tools falter
Despite over 1,300 FDA-approved devices, 77% of industry leaders cite immature tools as a barrier to adoption, prompting a shift toward bespoke generative AI
Health care providers are increasingly shifting strategy from purchasing generic off-the-shelf products to partnering with developers to create custom AI solutions. This move aims to address specific clinical and business needs, acknowledging that many existing applications fail because they lack a deep understanding of the complex health care environment. While the sector faces significant pressures including financial constraints, labour shortages, and the growing burden of caring for an aging population, the sector is looking to technology to navigate these challenges.
The U.S. Food and Drug Administration has approved more than 1,300 AI-enabled medical devices, with over half approved in the past three years, primarily for interpreting diagnostic images. Non-radiological AI applications include tracking sleep apnea, analysing heart rhythms, and planning orthopedic surgeries. However, AI applications that do not count as medical devices—such as those that handle scheduling and administrative tasks—are more difficult to track but are also rapidly increasing.
Steve Bethke, vice president of the solution developer market for Mayo Clinic Platform, emphasises that solution developers must align clinical and technical capabilities with relevant business impacts to ensure adoption. He notes that health care is very complex and that if developers miss any dimension, the solution will not be adopted or drive value. This sentiment is echoed by a recent survey of technology leaders which 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.
McKinsey research reveals that 61% of health care organisations intend to pursue partnerships with third-party vendors for customised generative AI solutions, rather than building in-house or buying off-the-shelf. This approach is designed to tackle technical challenges and position AI products for maximum impact and value, avoiding the pitfalls unique to the health care environment. Providers recognise that any health care-related application can potentially impact patient care, whether directly or indirectly.
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. 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.
Historical context notes that the earliest FDA-approved AI-enabled medical device dates back to 1995. While the regulatory landscape remains in flux, the industry is clear that 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.
