Comparative conceptual analysis reveals distinct implementation hurdles for adaptive AI in healthcare, suggesting frameworks require shifts toward lifecycle stewardship.
Key Points
To evaluate how various artificial intelligence systems align with or challenge fundamental assumptions of implementation science frameworks, using the Consolidated Framework for Implementation Research (CFIR) as a diagnostic tool.
Conducted a comparative conceptual analysis synthesizing foundational implementation science literature, complex intervention and digital health studies, and healthcare AI empirical research.
Applied the five core domains of the Consolidated Framework for Implementation Research (CFIR) as an analytical lens to identify areas of alignment and conceptual tension.
Static or locked AI tools align well with traditional digital interventions, whereas adaptive, generative, and data-dependent systems present distinct challenges including opacity, probabilistic outputs, performance drift, and vendor-driven updates.
Adaptive systems challenge core implementation science assumptions regarding intervention stability, clear boundaries, and evidentiary closure, demonstrating that AI deployment requires ongoing lifecycle stewardship rather than a one-time rollout.