Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Mar 31, 2025
Open Peer Review Period: Mar 10, 2026 - May 10, 2026
Date Accepted: Dec 17, 2025
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Dynamic Personalized Optimization: An AI Functionality Framework for Digital Therapeutics
ABSTRACT
Dynamic Personalized Optimization (DPO) is introduced as a conceptual framework that defines core AI functions required to deliver real-time, personalized and optimized treatment in digital therapeutics (DTx). DPO continuously refines therapeutic strategies by integrating patient data, treatment content, usage feedback, and status measurements to provide real-time, personalized treatment. Utilizing pre- dictive AI models, DPO adapts treatment approaches based on patient responses, thereby improving therapeutic effectiveness. Furthermore, this paper explores the potential role of large language models (LLMs) in supporting DPO by processing diverse and complex data formats. By addressing current lim- itations in real-time personalization within DTx, DPO establishes a structured, AI-driven approach to delivering tailored digital interventions. This framework ultimately aims to enhance treatment efficacy and improve patient engagement.
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Copyright
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