Consumer Trust and Purchase Intention toward AI-Driven Personalized Healthcare and Digital Therapeutics

Artificial Intelligence (AI) is transforming healthcare by enabling personalized support, digital therapeutics, and intelligent health management tools. From diabetes management applications to AI-powered medical chatbots, these technologies are increasingly influencing healthcare decisions. Despite rapid market growth, with the global AI healthcare market projected to exceed USD 110 billion by 2030, consumer adoption remains limited. A major factor underlying this hesitation is trust. This review examines the determinants of consumer acceptance of AI-driven healthcare applications and digital therapeutics. It aims to synthesize evidence from consumer behavior, health informatics, and digital health literature to identify factors that promote or hinder adoption. A scoping review was conducted following the PRISMA 2020 guidelines. Relevant studies published between 2018 and 2025 were identified through Scopus, Web of Science, PubMed, and Google Scholar. The review was guided by established theoretical frameworks, including the Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and Risk Perception and Trust Theory. Consumer trust increases when AI systems are perceived as useful, easy to use, transparent, and capable of protecting personal health data. Conversely, trust declines when concerns arise regarding privacy, algorithmic opacity, and the absence of human oversight. Demographic factors such as age, gender, culture, and digital literacy significantly influence trust formation and adoption behavior. Physician recommendations, user reviews, and media endorsements also shape consumer perceptions. The successful adoption of AI healthcare technologies depends on trust-centered design, transparent data practices, clinician involvement, and culturally sensitive implementation strategies.