Background/Objectives: The application of artificial intelligence (AI) in the healthcare field continues to deepen, with the development paradigm gradually shifting from technology-driven innovation to design-driven innovation towards “human-centered artificial intelligence (HCAI).” This aims to bridge the potential of AI technology with actual clinical needs and improve the quality and accessibility of healthcare services. However, it still faces challenges such as insufficient integration of theoretical frameworks, complex implementation challenges, and an imperfect ethical governance mechanism. Methods: This article presents a systematic narrative review about the philosophical and ethical foundations of HCAI related literature, analyzes the specific clinical application models recorded in the literature, integrates key theories related to implementation science, human–machine collaboration, and explainable AI(XAI), and constructs a multidimensional comprehensive analysis framework for HCAI in the medical field. Results: The study shows that design-driven innovation is the key to bridging the gap between the potential of AI technology and practical medical applications; the successful implementation of “human-centered artificial intelligence” relies on interdisciplinary collaboration, stakeholder co-creation, and ethical considerations throughout the entire lifecycle; Among them, human-centered design ensures that technology meets real needs; Implementation Science guarantees innovation can effectively integrate into complex medical environments; explainable AI technology is the cornerstone of establishing clinical trust; the strategic governance framework sets boundaries and tracks for the healthy development of the entire ecosystem. Conclusions: Beyond summarizing existing research findings, this study proposes targeted design frameworks and trade-off strategies for key technical and practical dilemmas of HCAI. It also clarifies the contextual boundaries of existing empirical results, provides a differentiated operational path for the implementation of HCAI, as well as a clear direction and important reference for academic research and future practical applications of human-centered AI medicine.
Liu Y (Wed,) studied this question.
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