Table 1 Study characteristics
From: A scoping review of human digital twins in healthcare applications and usage patterns
Author affiliation | n (%) |
Academic/Medical | 130 (87.25) |
Industry | 14 (9.40) |
Government | 5 (3.36) |
Design of model | n (%) |
Empirical | 64 (42.95) |
Mechanistic | 47 (31.55) |
Hybrid | 38 (25.50) |
Type of model | n (%) |
Personalized digital model, not used for decision support | 56 (37.58) |
Personalized digital model, used once for decision support | 31 (20.81) |
Digital twin; dynamically updated with human-in-the-loop recommendations | 17 (11.41) |
Virtual patient cohort | 15 (10.07) |
General digital model | 15 (10.07) |
Digital shadow; dynamically updated and not used for decision support | 14 (9.40) |
Digital twin; dynamically updated, automatic updates to physical system | 1 (0.67) |
Systems modeled | n (%) |
Cardiac | 43 (28.86) |
Metabolic | 19 (12.75) |
Musculoskeletal | 18 (12.08) |
Other | 14 (9.40) |
Cancer | 11 (7.38) |
Whole body | 10 (6.71) |
Respiratory | 9 (6.04) |
Neurological | 6 (4.03) |
Hepatic | 5 (3.36) |
Immune | 5 (3.36) |
Surgical site | 4 (2.68) |
Epidermal | 3 (2.01) |
Reproductive | 2 (1.34) |
