Translational artificial intelligence in gastrointestinal and hepatic disorders: Advancing intelligent clinical decision-making for diagnosis, treatment, and prognosis
Translational artificial intelligence in gastrointestinal and hepatic disorders: Advancing intelligent clinical decision-making for diagnosis, treatment, and prognosis
Shuqi Ren,Jin-Man Chen,Chuang Cai
TLDR
This article systematically reviews the latest advancements in AI technology concerning the diagnosis and treatment of gastrointestinal diseases and hepatic diseases, emphasizing its application value and transformative potential in critical areas such as imaging omics analysis, endoscopic intelligent identification, and personalized treatment prediction.
Abstract
Gastrointestinal and hepatic disorders exhibit significant heterogeneity, characterized by complex and diverse clinical phenotypes. Most lesions present without typical symptoms in their early stages, which poses substantial challenges for early clinical identification and intervention. As an interdisciplinary field at the forefront of technology, artificial intelligence (AI) integrates theoretical innovation, algorithm development, and engineering applications, triggering paradigm shifts within the medical field. Current research trends indicate that AI technology is progressively permeating the entire diagnostic and therapeutic process for gastrointestinal and hepatic disorders, facilitating intelligent transformations in precise lesion detection, optimization of treatment decisions, and prognosis evaluation through the integration of different modal data, construction of intelligent algorithms, and establishment of clinical verification systems. This article systematically reviews the latest advancements in AI technology concerning the diagnosis and treatment of gastrointestinal diseases (such as inflammatory bowel disease and digestive system tumors) and hepatic diseases (including hepato-cirrhosis and liver cancer), emphasizing its application value and transformative potential in critical areas such as imaging omics analysis, endoscopic intelligent identification, and personalized treatment prediction.
