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

Authors

Dohyoung Rim

Published in

JMIR medical informatics. Volume 14. Pages e78202. Sep 25, 2026. Epub Sep 25, 2026.

Abstract

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.

PMID:
42789929
Bibliographic data and abstract were imported from PubMed on 26 Sep 2026.

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