Bridging the Bench-to-Bedside Gap with Multimodal Artificial Intelligence in Digestive Diseases

This comment discusses a recent review by Wu and colleagues on multimodal artificial intelligence in gastroenterology and hepatology. The review outlined advancements in endoscopic, radiomics, pathologic, and multi-omics technologies. Additionally, it highlights persistent barriers, such as data heterogeneity, “black box” opacity, reimbursement uncertainty, and third-party data security risks. The comment also discusses current payment models for autonomous algorithms and emphasizes the importance of robust governance frameworks. Beyond summarizing recent progress, this commentary proposes a pragmatic, five-point roadmap to facilitate the safe and fair deployment of multimodal artificial intelligence in digestive disease care in the future, including standardization, explainability, federated governance, equitable reimbursement, and sustainability metrics. By implementing these action items, stakeholders can transform promising algorithms into clinically validated, workflow-compatible, and economically viable tools.