Healthcare—one of the core pillars of the United Nations’ Sustainable Development Goals—is being reshaped by the rapid spread of AI. While AI adoption offers substantial opportunities for innovation, it also introduces new challenges and disrupts established healthcare practices. Hence, understanding and documenting how healthcare organizations develop AI-related capabilities and translate them into organizational value is important. This study conducts a systematic literature review to examine how healthcare organizations build and leverage AI-enabled capabilities to create value. Following the PRISMA guidelines and drawing on dynamic capability theory as the analytical framework, I conducted a structured literature search using the keywords “artificial intelligence” and “healthcare capability.” The search encompassed open-access publications from PubMed and IEEE Xplore, as well as institutionally accessed studies from the AIS eLibrary, focusing on articles published between 2015 and 2025. Applying predefined inclusion and exclusion criteria resulted in a final sample of 102 articles. I employed qualitative analysis to systematically examine the selected studies. The analysis identifies key AI tools in healthcare, their underlying micro-foundations, the AI-enabled capabilities they support, the resulting healthcare outcomes, and the challenges shaping AI-enabled healthcare. Building on these findings, I propose a process model that explains how AI tools and micro-foundations enable sensing, seizing, and transforming capabilities, which in turn drive AI-enabled healthcare outcomes. These outcomes recursively reinforce and further develop AI-enabled healthcare capabilities. This study contributes to the literature on AI and dynamic capabilities in healthcare by clarifying the mechanisms through which AI creates value. From a practical perspective, it offers actionable insights for healthcare organizations seeking to operationalize AI effectively by clarifying how AI strengthens and extends healthcare capabilities.
Dereje Ferede (Fri,) studied this question.