Interoperability as a dynamic capability for artificial inteligence-augmented and sustainable health systems

Introduction: Healthcare systems face growing pressure to integrate artificial intelligence into clinical workflows, yet a fundamental governance gap constrains these tools' potential: a lack of interoperability across health information systems, institutions, and care levels. Objectives: This study examines how interoperability functions as a dynamic capability that enables synergistic collaboration between artificial and human intelligence in healthcare systems, and identifies governance, workforce, and implementation implications for health administrators at the national, regional, and institutional levels. Methodology: A systematic literature review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 statement analyzed 40 peer-reviewed studies published between 2020 and 2025, retrieved from Scopus, Web of Science, PubMed, and IEEE Xplore. Quality assessment by two independent reviewers using the Mixed Methods Appraisal Tool achieved 89% inter-rater agreement; thematic synthesis inter-coder reliability reached a Cohen's kappa of 0.78. Results: Three interconnected themes emerged: interoperability as a meta-governance mechanism, in which governance-first design is an empirical predictor of artificial intelligence implementation success; an artificial intelligence and human intelligence augmentation cycle, in which interoperable data foundations elevate clinical roles and generate new workforce competencies; and sustainable development goal pathways, with mental health data integration identified as a consequential and underexplored governance frontier. Complementary empirical evidence from a Brazilian reference hospital documents annual losses exceeding four million United States dollars attributable to interoperability failure. Conclusion: Interoperability must be conceptualized as strategic dynamic capability-building, not information technology infrastructure. Governance-first implementation, legal clarity, workforce development, and multilevel coordination are preconditions for equitable, artificial intelligence-enabled health systems.