DECIDE-Twin: A Framework for AI-Enabled Digital Twins in Clinical Decision-Making
Bibliographic record
Abstract
BACKGROUND: AI-enabled digital twins (DTs) are advanced virtual models of a complex real-world system, which have the potential to transform clinical decision-making. Despite the growing interest in such DTs, the literature lacks a unified framework for their 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 their development and implementation. METHODS: Informed by frameworks established by Arksey and O'Malley, and the Joanna Briggs Institute, we performed a scoping review of studies on the development and implementation of AI-enabled DTs for clinical decision-making in any healthcare setting. The search strategy was developed by a librarian for three databases from the date of inception until August 2023. We also conducted a grey literature search on Google Scholar. One reviewer screened titles and abstracts, full-text articles, and charted data, and the second reviewer verified them. Quantitative data were summarized using frequency and proportions, and qualitative data were summarized using content analysis. Key steps in DT development were identified to create the DECIDE-Twin framework. RESULTS: Eleven articles were included: seven reviews and four empirical studies. The reviews contained either a framework or information that was used to construct our comprehensive framework. The empirical studies reported the DT development, and one reported a common infrastructure for a wide range of DT applications. CONCLUSION: We developed the DECIDE-Twin framework that could serve as a guide for researchers and practitioners in DT development and implementation for clinical decision-making. Further research is needed to validate and implement this framework for various clinical applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.071 | 0.103 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".