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This study maps how Artificial Intelligence is theorized and implemented within Health Information Systems from a management perspective. Using the Web of Science Core Collection, we analyze 323 English-language articles and reviews published between 2020 and 2025 (book chapters and retracted items excluded). Performance indicators and science-mapping techniques—co-word, co-citation, and bibliographic coupling—were conducted in VOSviewer. Results show rapid growth after 2020 and sustained scholarly visibility. The field is built on three pillars: data and system foundations enabling AI, including big data and governance; machine-learning decision support guided by workflow fit, explainability, fairness, and accountability; and AI deployment in electronic health records, accelerated by COVID-19 and formalized through product-management practices. Journal and publisher profiles concentrate in informatics and open-science venues, while country coupling reveals internationally integrated reference ecosystems. The study specifies the micro-foundations of AI capability, articulates governance for hybrid human–AI decisions, and frames the EHR as a platform for scalable analytics. We offer a reproducible protocol and a management-centered agenda for reliable, equitable, and governable AI in health systems.
Alnahdi et al. (Mon,) studied this question.