Balancing the Triad: Socio-Technical Misalignments Between User-Centered Design, Agile, and AI in Healthcare Software

Abstract

The rapid integration of Artificial Intelligence (AI) into healthcare software introduces profound complexities when combined with Agile Software Development (ASD) and User-Centered Design (UCD). Instead of a cohesive triad, this landscape exhibits partial intersections and fragmented literature. This Systematic Literature Review (SLR) investigates how competing priorities within the ASD-UCD-AI triad create socio-technical misalignments, impacting user experience and clinical implementation. An analysis of 27 primary studies, backed by a rigorous quality assessment, systematically weights the corpus's evidentiary strength. Through a transparent synthesis pipeline based on the extraction of verbatim excerpts, generation of inductive codes, and thematic grouping, this study uncovers four core socio-technical tensions: (1) Technical Accuracy vs. Contextual Value; (2) Agile Delivery Speed vs. Clinical Safety Compliance; (3) AI Automation vs. Human Explainability; and (4) High-Level AI Ethics vs. Daily Agile Practices. While high-quality studies primarily defined these dimensions, lower-quality evidence provided contextual background. This review demonstrates that failing to balance these tensions relegates robust models to a "model graveyard." Finally, the study consolidates actionable mitigation strategies, such as redefining multidisciplinary Scrum roles and implementing continuous design controls via pull requests. By bridging theoretical ethical guidelines and practical software engineering, this SLR provides an evidence-informed analytical lens to develop safe, user-centric, and agile AI healthcare systems.