SAFE_DTx: Safety-First Framework for AI-Driven Personalization in Digital Therapeutics.

Digital therapeutics (DTx) are emerging as evidence-based software interventions, but current AI-driven personalization approaches lack dedicated safety-focused frameworks and face challenges due to scarce long-term outcome data and unpredictable model behaviors. We propose SAFE_DTx, a safety-first architectural framework for DTx that integrates well-established principles of predictive modeling and constrained decision-making to prioritize patient safety. SAFE_DTx's 2-module architecture comprises an AI feedback prediction module that forecasts short-term patient responses and a constrained planning module that selects the next intervention under explicit safety constraints. By decoupling these components and enforcing clear safety guardrails, the framework enables dynamic, real-time adaptation to individual patient feedback while staying within evidence-based safety limits. This modular design also enhances transparency in the decision-making process, and an in silico evaluation demonstrates its preliminary architectural feasibility, showing greater engagement and no safety violations compared to baseline strategies within the simulated environment. SAFE_DTx's safety-by-design architecture aligns with emerging regulatory emphasis on AI transparency and patient safety. It directly addresses key clinical challenges in AI-driven DTx personalization by ensuring that tailored interventions do not compromise patient safety.