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

BACKGROUND

AI-enabled digital twins (DTs) are advanced virtual models of a complex real-world system, augmented with predictive AI capabilities, which have a potential for transforming healthcare and clincial decision making. Despite the growing interest and need in such DTs, the literature lacks a unified framework for its development and implementation.

OBJECTIVE

This study aims to map the existing knowledge on AI-enabled DTs for clinical decision-making, and develop a comprehensive framework for its development and implementation.

METHODS

Informed by the scoping review frameworks established by Arksey and O'Malley, and the Joanna Briggs Institute, we performed a scoping review of studies reporting on the development and implementation of AI-enabled DTs for clinical decision-making in any healthcare setting. The search strategy was developed by a professional academic librarian for three databases, i.e., PubMed, Embase (Ovid), and IEEE Xplore, from the date of inception until August 2023. We also conducted a grey literature search on Google Scholar. Moreover, we scanned the references of all included studies to identify additional potentially relevant studies for inclusion. A reviewer screened titles and abstracts, full-text articles, and charted data, and the second reviewer verified them. Data were summarised descriptively using content analysis. Key steps in DTs development were identified and combined to create the DECIDE-Twin framework.

RESULTS

Out of 421 records, 11 were included: seven reviews and four empirical studies, all published from 2020 onward. The reviews contained either a framework or information that was used to construct our newly developed framework. The empirical studies reported the development of DTs, and one reported a common infrastructure for a wide range of applications for DTs.

CONCLUSION

We developed the DECIDE-Twin framework that could serve as a guide for researchers and practitioners for the development and implementation of AI-enabled DTs in clinical decision making. Further research is needed to validate and implement this framework across various use cases.