The examination of short-term performance metrics, such as error rates and efficiency gains, primarily drives the integration of generative artificial intelligence (AI) into healthcare. This perspective may overlook the potential long-term systemic effects that could arise from the widespread incorporation of AI-generated content into clinical documentation and decision-support workflows. To present the algorithmic bioaccumulation model (ABM), which serves as a conceptual framework for studying the health system accumulation of small, incremental AI-mediated informational changes over time. We performed a narrative-focused interdisciplinary review guided by theory, synthesizing the literature between 2015 and 2025 across healthcare informatics, cognitive psychology, machine learning, and environmental epidemiology domains. It included earlier pre-2015 theoretical literature to provide context on the concepts. From this foundation, the ABM theorizes cumulative informational effects occurring in three interrelated phases: (1) digital sedimentation, or the gradual settling of AI-propagated or template-generated content into electronic health records (EHRs); (2) clinical deskilling, or potential changes in clinician cognition rooted in increased dependence on automated summaries and decision-support outputs; and [(3) system/model degradation as a downstream scenario, whereby increasing exposure to AI-element datasets may impact data-driven system performance and variability. The ABM underscores the importance of considering healthcare AI integrations through a cumulative systems lens. Future research should investigate the measurable characteristics of informational accumulation and governance approaches that prioritize transparency, provenance tracking, and meaningful human oversight within AI-enabled clinical workflows.
Vijayakumar et al. (Tue,) studied this question.
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