Digital twins in gastroenterology: From computational modeling to precision medicine and clinical translation

Table 1 Traditional predictive model vs digital twin

Feature	Traditional predictive model	Gastriontestinal digital twin
Data basis	Population cohort (group-level coefficients)	Individual patient (multimodal, longitudinal)
Temporal score	Single time point; static after deployment	Continuous; updated with each new data input
Update mechism	None (model frozen post-training)	Data assimilation (e.g., ensemble Kalman filter, Bayesian methods)
Intervention testing	Not possible	In silico simulation of drug, dose, or procedure before clinical use
Model architecture	Statistical/data-driven (regression, ML)	Mechanistic, data-driven, or hybrid (physics-informed neural networks)
Personalization	Risk score adjusted by a few covariates	Full virtual replica of individual patient physiology
GI example	Biologic response score (single clinic visit)	IBD immune twin; gastric motility CFD model; microbiome MCMM
Key limitation	Cannot adapt to evolving disease; population averages mask individual variation	Data requirements high; computational infrastructure; validation gap

The bidirectional data loop (afferent/efferent) is the defining architectural feature that distinguishes a digital twin from any static predictive model. CFD: Computational fluid dynamics; MCMM: Microbial community-scale metabolic model; IBD: Inflammatory bowel disease; ML: Machine learning; GI: Gastrointestinal.