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DECIDE-Twin: A Framework for AI-Enabled Digital Twins in Clinical Decision-Making.

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    This study introduces the DECIDE-Twin framework for developing AI-enabled digital twins (DTs) in healthcare. This framework guides researchers and clinicians in implementing DTs for improved clinical decision-making.

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    Area of Science:

    • * Digital Health
    • * Artificial Intelligence
    • * Clinical Decision Support

    Background:

    • * AI-enabled digital twins (DTs) offer transformative potential in healthcare decision-making.
    • * A unified framework for DT development and implementation is currently lacking in the literature.
    • * Growing interest necessitates structured approaches for AI-driven virtual models in clinical settings.

    Purpose of the Study:

    • * To systematically map existing knowledge on AI-enabled DTs for clinical decision-making.
    • * To develop a comprehensive framework for the creation and deployment of these advanced virtual models.
    • * To address the gap in unified guidance for AI-DTs in healthcare.

    Main Methods:

    • * Conducted a scoping review following established frameworks (Arksey and O'Malley, JBI).
    • * Searched multiple databases (PubMed, Embase, IEEE Xplore) and grey literature until August 2023.
    • * Employed dual reviewer screening and content analysis to synthesize findings and develop the DECIDE-Twin framework.

    Main Results:

    • * Included 11 studies (7 reviews, 4 empirical) published from 2020 onwards.
    • * Identified key development steps from existing frameworks and empirical studies.
    • * Synthesized information to create the novel DECIDE-Twin framework.

    Conclusions:

    • * The DECIDE-Twin framework provides a structured guide for developing and implementing AI-enabled DTs in clinical decision-making.
    • * This framework can assist both researchers and practitioners in the healthcare domain.
    • * Further validation and application across diverse clinical use cases are recommended.