Digital health technologies may be technically effective yet remain underused when intended users perceive limited value, experience excessive effort, lack confidence or support, distrust the system, or encounter poor workflow fit. This practitioner-oriented narrative review explains seven widely used frameworks for studying these problems: the Technology Acceptance Model, the Unified Theory of Acceptance and Use of Technology 2, Diffusion of Innovations, the Theory of Planned Behavior, Task-Technology Fit, the Health Belief Model, and Social Cognitive Theory. Original theory publications were combined with recent systematic reviews, meta-analyses, and selected Gulf Cooperation Council evidence. The review distinguishes behavioral intention, actual use behavior, continuance intention, and continued use; explains predictor, outcome, mediator, and moderator roles; and identifies conceptual overlap among common adoption constructs. It proposes an outcome-first decision process for selecting a model and recommends adding artificial-intelligence-specific constructs, including trust, risk, accountability, and human oversight, when appropriate. The review further argues that adoption should be evaluated before, during, and after implementation using measures aligned with the technology's intended task and clinical or service purpose. Implications include contractual access to meaningful usage data, shared accountability between vendors and healthcare organizations, and stronger Arabic localization and comprehension testing. The review concludes that adoption models are most useful when they diagnose specific, actionable causes of nonuse rather than treating low uptake as generalized resistance to change.
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