Artificial Intelligence in Healthcare: Strategic Value, Constraints, and a Governance-First Integration Framework

Abstract

Evidence from peer-reviewed studies and credible reports indicates that AI in healthcare most consistently delivers value through four themes—efficiency, cost reduction, competitive differentiation, and new service models—while realized impact is moderated by data governance/privacy, explainability & accountability, and organizational readiness. Reported effects commonly include 10–30% reductions in prediction error (diagnostics/forecasting) and 20–40% decreases in administrative minutes, which under conservative mappings correspond to ≈2–4% operational savings. Guided by these findings, we present a governance-first integration framework for clinical, administrative, and operational settings that specifies: (i) investment in data infrastructure and measurable SLOs; (ii) staged pilots using explicit clinical, operational, and economic metrics; and (iii) capability building and incentive alignment for scale. A concise evaluation agenda (cost-effectiveness, quasi-experimental designs, fidelity reporting) is outlined to move beyond descriptive claims, and a brief case illustrates how governance choices shape performance and adoption. The paper provides a practical roadmap that keeps findings central while translating them into actionable governance and evaluation steps.

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