Adoption of artificial intelligence in healthcare: survey of health system priorities, successes, and challenges

Ambient Notes, a generative AI tool for clinical documentation, has rapidly advanced and demonstrated early success. Its uniform early adoption, in contrast to other AI use cases, suggests it has crossed the 'chasm' from early adopters to the pragmatic early majority. This rapid adoption is likely influenced by concerns over clinician burnout and the need for improved workflow efficiency, similar to the historical adoption of CT and MRI technologies.

Key barriers to AI adoption include immature AI tools, financial concerns, and regulatory uncertainty. Less significant barriers were lack of clinician adoption, insufficient in-house expertise, and lack of leadership support. Addressing these requires robust evaluations, shared strategies, common deployment platforms for AI tools, and enhanced governance models to monitor differential performance and impact, especially regarding health equity and bias.

While AI, particularly generative AI, holds significant promise for transforming healthcare, its widespread adoption faces unique sociological and technical challenges. Future research is crucial to evaluate the impact of AI on organizational and clinical outcomes, including clinician productivity, retention, and patient satisfaction. National bodies and consortia need to share expertise, disseminate evaluation results, and develop practical approaches for monitoring AI performance across vulnerable populations to ensure responsible and equitable integration.