This study investigates the multifaceted factors shaping user experience in the adoption of AI-based information systems within healthcare settings. As artificial intelligence increasingly supports clinical decision-making processes, understanding the psychological and behavioral dimensions of user interaction becomes imperative. Utilizing a qualitative systematic literature review approach, this research synthesizes findings from scholarly articles published between 2020 and 2025. The analysis reveals three critical determinants influencing user experience: trust in AI, perceived usefulness, and ease of use. These factors play a central role in shaping technology acceptance, which acts as a mediating variable linking system attributes to overall user experience. Furthermore, digital literacy emerges as a moderating factor that either amplifies or diminishes the effects of the core determinants. To provide a comprehensive framework, the study integrates theoretical constructs from the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and Trust in Automation Theory. The resulting conceptual model underscores the interplay between user perceptions, cognitive processes, and external technological factors. Key findings emphasize the significance of user-centered system design, transparent and interpretable AI communication, and targeted digital literacy initiatives to promote broader and more effective adoption. From a theoretical standpoint, the research contributes to the evolving literature by combining behavioral science perspectives with information system adoption theories. Practically, the study offers valuable insights for system developers, designers, and healthcare institutions aiming to implement AI technologies effectively. Recommendations include fostering transparency in AI decision-making, ensuring intuitive system interfaces, and offering digital competency training tailored to diverse user profiles. Overall, this study underscores that a nuanced understanding of user-related factors is essential for maximizing the potential benefits of AI in healthcare and achieving sustainable digital transformation in clinical environments.