AI- and Generative AI-Driven Digital Therapeutics: A Critical Narrative Review of Emerging Evidence

Background: Artificial intelligence-driven digital therapeutics (AI-DTx) are rapidly emerging as a transformative paradigm in healthcare, integrating machine learning, deep learning, and generative AI into digital interventions across diverse clinical domains. Despite rapid growth, the evidence landscape remains fragmented, with heterogeneous methodologies, diverse application contexts, and limited cross-domain synthesis. Aim: This narrative view aims to provide an evidence-informed narrative synthesis of the available secondary literature on AI-driven digital therapeutics, primarily focusing on systematic reviews and meta-analyses, to identify emerging patterns, cross-cutting trends, and future directions across clinical and technological domains. Methods: A narrative synthesis of secondary evidence was conducted, focusing on 23 systematic reviews, meta-analyses, and relevant review articles addressing AI-driven digital therapeutics. The identified literature was analyzed to explore recurring themes across clinical domains, technological approaches, and implementation challenges. Findings were further contextualized through selected recent randomized controlled trials and translational studies to provide insights into emerging clinical applications and real-world perspectives. Results: Across the available literature, AI-driven digital therapeutics demonstrate a broad and rapidly evolving expansion across mental health, chronic disease management, rehabilitation, and behavioral health. The field is characterized by a progressive shift from static, rule-based interventions toward more adaptive systems supported by machine learning, deep learning, and generative AI. A key emerging theme is the role of AI as an enabling layer for personalization, adaptation, and dynamic intervention delivery rather than as a standalone therapeutic modality. Mental health represents the most extensively studied domain, particularly through conversational agents and cognitive behavioral therapy-informed interventions, while other clinical areas are progressively expanding their translational potential. Persistent challenges include methodological heterogeneity, limited long-term validation, and incomplete integration into routine clinical workflows. Discussion: The current evidence suggests a transition toward hybrid human–AI models of care, in which digital systems may support and augment clinical practice through adaptive and data-driven approaches. However, the field remains characterized by fragmented evidence, evolving evaluation approaches, and challenges related to standardization, validation, and real-world implementation. Conclusions: AI-driven digital therapeutics are evolving toward increasingly adaptive and clinically oriented healthcare solutions. Future progress will depend on improving methodological consistency, strengthening long-term evaluation, and supporting responsible integration into clinical pathways to ensure safe, scalable, and meaningful impact.