Human Digital twins in Healthcare: Current Reality Falls Short of Potential, new Review Finds
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Washington D.C. – A newly published scoping review reveals that while interest in Human Digital Twins (HDTs) – virtual replicas of individual patients – is surging in healthcare,the vast majority of current research doesn’t meet the rigorous standards required for true clinical application. The study, examining literature from January 2017 to July 2024, highlights a notable gap between the promise of HDTs and their current implementation.
What are Human digital Twins?
digital twins, broadly, are dynamic virtual models mirroring physical systems. In healthcare, HDTs aim to provide personalized, continuously updated, and predictive insights to inform medical decision-making. The National Academies of sciences, Engineering, and Medicine (NASEM) has established a key definition: a true digital twin must possess all three of these characteristics to be clinically useful.
the Scoping Review Findings
researchers conducted a systematic literature search and analyzed 149 studies.The results were stark. Only 18 studies - a mere 12.08% - fully aligned with the NASEM definition of a digital twin. The remaining studies fell into other categories:
- Digital shadows (9.4%): These models lack dynamic updating capabilities.
- General Digital Models (10.07%): these are broader models not personalized to individual patients.
- Virtual Patient Cohorts (10.07%): These represent groups of patients,rather than individual virtual counterparts.
A Critical Missing Piece: Validation and Reliability
Perhaps the most concerning finding is the limited focus on model reliability. Only two studies mentioned Verification, Validation, and Uncertainty Quantification (VVUQ) – a crucial NASEM standard for ensuring the accuracy and trustworthiness of digital twin predictions. Without robust VVUQ, the clinical utility of HDTs remains questionable.
implications for the Future
this review underscores the need for more rigorous research and progress in the field of HDTs. Moving forward, studies must prioritize personalization, dynamic updating, predictive capabilities, and, critically, thorough validation to unlock the full potential of these technologies for improving patient care.
Source: Scoping review findings published [Date of publication – *add if available from original source*]
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