Human Digital Twins for pervasive healthcare: A scoping review
Bibliographic record
Abstract
Background: Human Digital Twins (HDTs) have recently emerged, especially in the context of healthcare. With the growing emphasis on preventive healthcare beyond diagnosis, pervasive sensing has become essential which enables continuous monitoring through real-world data captured from wearables and/or mobile devices. Objective: This scoping review investigates how pervasive sensing technologies have been utilized in the implementation of HDTs for healthcare, with a focus on understanding the twinning methods, identifying their advantages and limitations, and uncovering key challenges encountered in real-world applications. Methods: We proposed an analytical framework to examine how pervasive sensing technologies are utilized in the implementation of HDTs for personal health management. Using this framework, we conducted a comprehensive literature search across PubMed, Scopus, IEEE Xplore, Web of Science, and Google Scholar. Results: A total of 39 eligible papers were reviewed. We present an analysis of these studies and provide a discussion on the potential and limitations of HDTs in the context of pervasive healthcare. Conclusions: The key takeaway is that the integration of HDTs and pervasive sensing provides a foundation for realizing pervasive healthcare by enabling not one-time digital replication, but continuous and comprehensive monitoring of individuals, including their surrounding environments and behavioral changes.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".