<![CDATA[Artificial intelligence (AI) is rapidly evolving in medicine, particularly in gastroenterology, a field heavily reliant on image analysis and clinical data. Technologies such as computer-aided detection (CADe) and computer-aided diagnosis (CADx) have been developed to enhance the detection and characterization of gastrointestinal (GI) lesions. This literature review aims to evaluate the roles, applications, benefits, and limitations of AI in gastroenterology. Utilizing a narrative synthesis approach, relevant primary studies from the past 5 to 10 years were retrieved from PubMed, Google Scholar, ScienceDirect, and Wiley Online Library. The findings indicate that AI is predominantly applied in endoscopic procedures, significantly improving the adenoma detection rate (ADR), polyp detection rate (PDR), and adenomas per colonoscopy (APC), while reducing the adenoma miss rate (AMR). Furthermore, AI demonstrates substantial potential in polyp characterization, upper GI cancer detection, capsule endoscopy interpretation, ulcerative colitis evaluation, and ultrasound-based hepatobiliary diagnosis. Despite these promising results, clinical implementation faces several challenges, including false-positive rates, the need for external validation, dataset biases, performance variability across different populations, high costs, and regulatory hurdles. In conclusion, AI holds immense potential to elevate the quality of diagnostics and patient care in gastroenterology. While highly effective as a clinical support tool, further research and robust regulatory frameworks are essential to ensure its optimal, safe, and sustainable integration into routine clinical practice.]]>
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