AI-Enhanced Personalization in Digital Therapeutics: A Framework Prioritizing Patient Safety
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By
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Dohyoung Rim
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September 25, 2026
Clinical Report: AI-Enhanced Personalization in Digital Therapeutics
Background
Digital therapeutics represent a significant shift in healthcare, offering software interventions for various conditions. However, the integration of AI for personalization raises concerns about patient safety.
Data Highlights
No numerical data or trial data is provided in the source material.
Key Findings
- SAFE_DTx is a safety-oriented optimization framework for digital therapeutics.
- The framework focuses on intermediate feedback signals rather than solely on final clinical outcomes.
- AI modules within SAFE_DTx estimate probable short-term feedback responses for therapy options.
- The approach emphasizes human oversight in AI-enabled therapies.
- Current DTx personalization methods often lack adequate safety measures.
Clinical Implications
The SAFE_DTx framework offers a structured approach to enhance patient safety in AI-driven digital therapeutics.
Conclusion
The SAFE_DTx framework provides a structured approach to integrating AI in digital therapeutics.
Related Resources & Content
- Wang C, Lee C, Shin H, NPJ Digit Med, 2023 -- Digital therapeutics from bench to bedside
- Challen R, et al., BMJ Qual Saf, 2019 -- Artificial intelligence, bias and clinical safety
- Frontiers in Psychiatry — Editorial: The Role of Human-Computer Interaction and Human Factors in the Future of Digital Therapeutics for Mental Health
- Frontiers in Digital Health — Responsible data selection method for algorithmic personalization of health apps: a case study on promoting mental health
- Frontiers in Digital Health — Enhancing Clinical Drug Development with AI: Strategies to Mitigate Data Bias, Bridge the Digital Divide, and Include Underrepresented Patient Groups
- Stat News — Using AI in addiction medicine could be particularly risky
- Artificial Intelligence-Enabled Medical Devices | FDA
- Effectiveness Of Digital Therapeutics On Chronic Pain In Older People: A Systematic Review And Meta-Analysis - PMC
- Recommendations of the ECNP Digital Network for the evaluation of mental health care apps: A Delphi consensus - ScienceDirect
Based on findings from:
SAFE_DTx: Safety-First Framework for AI-Driven Personalization in Digital Therapeutics
Dohyoung Rim. Jmir Medical Informatics, 2026.
https://medinform.jmir.org/2026/1/e78202
This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.
