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

Abstract

Digital twin technology, an emerging paradigm in medicine, has the potential for creating dynamic virtual models that integrate multi-modal data to simulate disease trajectories and therapeutic responses of patients. A gastrointestinal (GI) digital twin combines data from electronic health records, imaging, microbiomes and real-time physiological inputs into computational frameworks that include mechanistic modelling, machine learning and hybrid approaches. These systems ensure bi-directional data flow, which allows continuous recalibration, enabling in-silico testing of therapeutic interventions before their implementation in the real-world. In this review, we summarize the current evidence for the architecture, methodologies, and clinical aspects of digital twins in various GI diseases. These advances are promising, but there are still several limitations, such as the integration of data, poor validation across diverse populations, computational demands and ethical concerns around privacy and bias. Most existing models have been limited to research environments. Future directions include the integration of wearable data, the development of multi-organ digital twins, and the incorporation of large language models for data harmonization and the implementation of federated learning frameworks. Digital twins hold immense potential to revolutionize gastroenterology through predictive, personalized, and simulation-guided clinical decision making. However, rigorous validation, standardization and clinical integration need to be ensured before their widespread adoption.

Keywords: Digital twin; Virtual patient; Personalized medicine; Computational modeling; Systems biology; In silico modeling; Artificial intelligence; Machine learning; Predictive modeling

Core Tip: Digital twins are a transformative shift in the field of gastroenterology as they allow dynamic, patient-specific virtual models that combine multimodal clinical, imaging and multi-omics data to mimic disease evolution and response to therapy. Digital twins provide a pathway for truly personalized and predictive care, as they can be updated in real-time and used for in silico testing of intervention strategies, unlike traditional predictive models. Emerging applications in hepatology, gastrointestinal oncology, inflammatory bowel disease, motility disorders and microbiome science underscore their potential. However, challenges related to data integration, validation, scalability and ethical governance need to be addressed before clinical implementation.