Artificial Intelligence for Investment Management in Healthcare: A Bibliometric Review and Strategic Perspectives
Artificial Intelligence for Investment Management in Healthcare: A Bibliometric Review and Strategic Perspectives

Summary/Abstract: The increasing complexity of healthcare systems and the growing need for efficient resource allocation have intensified the relevance of artificial intelligence (AI) in strategic and investment decision-making. While previous research has primarily focused on the clinical applications of AI, limited attention has been given to its potential in healthcare investment management. The lack of integrative analyses linking technological, managerial, and economic dimensions highlights a critical research gap in understanding AI’s strategic role within health governance. This study aims to provide a comprehensive assessment of the current state, structure, and evolution of research on AI applications in healthcare investment management. Using data from the Scopus database (1989–2024), the analysis integrates systematic literature selection under the PRISMA protocol, bibliometric mapping via VOSviewer, and strategic interpretation through SWOT analysis. The results reveal a clear expansion of research activity since 2016, with exponential growth after 2020, coinciding with the rise of digital transformation initiatives. Four major thematic clusters were identified – technological, managerial–investment, clinical, and strategic – illustrating the multidisciplinary nature of the field. The findings indicate that AI is evolving from an analytical tool to a strategic enabler of decision-making, investment optimisation, and governance reform in healthcare systems. The SWOT analysis further highlights that while the field demonstrates strong interdisciplinary growth and managerial relevance, it faces challenges such as limited empirical validation, ethical and governance concerns, and uneven digital readiness across contexts. These insights align with emerging studies emphasising the need for balanced AI integration that enhances both efficiency and human-centric values in healthcare management. In conclusion, the study establishes a strategic framework linking bibliometric evidence with policy and managerial implications, suggesting future research directions focused on the development of integrated models that combine technological innovation, ethical governance, and economic sustainability in AI-driven healthcare investment management.

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