Emerging Role of AI in Gastroenterology and Hepatology: Revolutionizing MedicalDevice-Assisted Diagnosis

Recent advancements in AI have emerged in the diagnosis of different diseases by enhancing the analysis of various medical imaging. Similarly, the engagement of AI in gastroenterology and hepatology is changing the methods of diagnosis and developing new computational methodology for more specific and targeted analysis of different medical devices. AI applies new algorithm-based approaches such as machine learning, deep learning, and language processing for the histopathological assessment, endoscopic imaging, and radiological assessment. New endoscopic imaging techniques are more specific for the detection of polyp rates, thus increasing targeted screening of colorectal cancer. Another approach, like AI-enabled imaging techniques, makes it possible for the early detection and targeted staging of hepatic disorders such as nonalcoholic fatty liver disease (NAFLD), cirrhosis, and hepatocellular carcinoma (HCC). In histopathological assessment, AI automated the high-throughput detection of malignancies in cells. The review paper enlightened on the application of AI in characterization, prognostication, and detection of GI and Hepatobiliary disorders. The engagement of AI enhances the diagnostic efficacy, reduces the chances of human error, and enlightens the therapeutic intervention. However, the AI-based techniques are limited in performance and have not reached the mark in regulatory constraints. Future developments should emphasize cross-platform interoperability, algorithmic improvement, and clinician education to optimize AI's therapeutic use in hepatology and gastroenterology.