Table 1_Beyond the black box in healthcare operations: a structured literature review and governance framework for explainable AI in clinical process intelligence systems.docx

Screening and extraction used transparent procedures adapted from PRISMA flow-reporting practices. Two authors independently screened records and extracted domain, XAI method, setting, evaluation, and governance variables; disagreements were resolved by author adjudication. Framework development used a documented hybrid deductive-inductive coding pipeline, source-contribution rules, negative-case review, and an auditable evidence-to-framework mapping anchored in IT Capability Theory, the DeLone–McLean IS Success Model, implementation science, sociotechnical systems theory, and health AI governance guidance.</p>Results<p>Of 187 included studies, 175 (93.6%) addressed diagnostic, disease-specific, imaging, or general clinical decision-support contexts, while operational and workflow-embedded AI accounted for 9 studies (4.8%).

The primary result is descriptive: XAI research in healthcare remains concentrated in diagnostic domains, while explicit explainability-governance work for clinical operations is sparse within the search boundary. Five evidence gaps informed the framework: limited operational XAI evaluation standards, role-blind explanation design, weak causal and counterfactual support, poor workflow embedding, and absence of longitudinal XAI governance.

A preliminary 0–15 scoring rubric and two hypothetical organizational assessments demonstrate how the framework can be applied analytically; they do not constitute external validation.</p>Conclusion<p>Healthcare XAI remains concentrated in diagnostic and clinical decision-support contexts, while explicit explainability-governance research for operational AI systems remains thinly represented. The CPI-XAI Governance Framework addresses this gap by treating explainability as an organizational capability rather than a model-level technical feature.

The framework is preliminary and theory-informed; it should be empirically validated before use as a measurement instrument.</p>