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 Scorecard: AI-Enhanced Personalization in Digital Therapeutics: A Framework Prioritizing Patient Safety
At a Glance
| Category | Detail |
|---|---|
| Condition | Digital Therapeutics |
| Key Mechanisms | AI-driven personalization with a focus on patient safety through intermediate feedback signals and safety constraints. |
| Target Population | Patients requiring digital therapeutic interventions for conditions like ADHD and chronic pain. |
| Care Setting | Digital health interventions |
Key Highlights
- SAFE_DTx framework prioritizes patient safety in digital therapeutics.
- Focus on intermediate feedback signals rather than solely final clinical outcomes.
- AI module estimates short-term feedback responses for therapy options.
- Emphasis on human oversight in AI-enabled therapies.
- Integration of established principles from operations research and safe reinforcement learning.
Guideline-Based Recommendations
Diagnosis
- Utilize evidence-based software interventions for conditions like ADHD and chronic pain.
Management
- Incorporate AI-driven personalization while maintaining explicit safety constraints.
Monitoring & Follow-up
- Track intermediate feedback signals to inform therapeutic decisions.
Risks
- Address unpredictability of complex AI systems that may jeopardize patient safety.
Patient & Prescribing Data
Individuals with conditions suitable for digital therapeutic interventions.
AI personalization should be guided by safety measures and feedback mechanisms.
Clinical Best Practices
- Implement offline learning and conservative Q-learning to enhance safety in AI applications.
- Use simulation-based optimization to support safe DTx personalization.
- Ensure that AI-driven interventions retain human oversight to mitigate risks.
Related Resources & Content
- Digital therapeutics from bench to bedside
- Artificial intelligence, bias and clinical safety
- A primer on reinforcement learning in medicine for clinicians
- Unleashing the potential of reinforcement learning for personalizing behavioral transformations with digital therapeutics
- A comprehensive survey on safe reinforcement learning
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.
