More than a chatbot: a practical framework to harness artificial intelligence across key components to boost digital...

Area of Science:

• Digital therapeutics
• Artificial Intelligence in healthcare
• Mental health interventions

Background:

• Advancements in AI-powered large language models show potential for AI-based chatbots in digital therapeutics (DTx).
• Realizing AI's full potential in DTx requires understanding intervention mechanisms, effectiveness drivers, and strategic AI integration.

Purpose of the Study:

• To present a practical framework for leveraging AI to improve the quality of digital therapeutics.
• To deconstruct DTx into five core components for targeted AI application.

Main Methods:

• Dismantling digital therapeutics into five key components: Therapeutic Units, Decision Maker, Narrator, Supporter, and Therapist.
• Detailing how AI can be applied to each component for enhanced functionality and personalization.

Main Results:

• AI can personalize Therapeutic Units through dynamic content adaptation.
• AI-driven Decision Makers can optimize therapeutic pathways using real-time data and adaptive algorithms.
• AI can enhance the Narrator, Supporter, and Therapist roles for personalized, scalable, and interactive therapeutic dialogues.

Conclusions:

• The proposed framework offers a structured approach to integrate AI into DTx, enhancing personalization and effectiveness.
• This modular framework allows for focused optimization of specific DTx components using AI.