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The prediction and final survival rate of gastrointestinal cancers are dependent on the stage of disease. The ideal would be to detect those gastrointestinal lesions at early stage or even premalignant forms which are difficult to detect by conventional endoscopy with white light optical imaging as they show minimum or no changes in morphological characteristics and are thus left untreated. The introduction of molecular imaging has greatly changed the pattern for detecting gastrointestinal lesions from purely macroscopic structural imaging to the molecular level. It allows microscopic examination of the gastrointestinal mucosa with endoscopy after the topical or systemic application of molecular probes. In recent years, major advancements in endoscopic instruments and specific molecular probes have been achieved. This review focuses on the current status of endoscopic imaging and highlights the application of molecular imaging in gastrointestinal and hepatic disease in the context of diagnosis and therapy based on recently published literature in this field. We also discuss the challenges of molecular endoscopic imaging, its future directions and potential that could have a tremendous impact on endoscopic research and clinical practice in future. [ABSTRACT FROM AUTHOR]

Artificial intelligence is a strong focus of interest for global health development. Diagnostic endoscopy is an attractive substrate for artificial intelligence with a real potential to improve patient care through standardisation of endoscopic diagnosis and to serve as an adjunct to enhanced imaging diagnosis. The possibility to amass large data to refine algorithms makes adoption of artificial intelligence into global practice a potential reality. Initial studies in luminal endoscopy involve machine learning and are retrospective. Improvement in diagnostic performance is appreciable through the adoption of deep learning. Research foci in the upper gastrointestinal tract include the diagnosis of neoplasia, including Barrett's, squamous cell and gastric where prospective and real-time artificial intelligence studies have been completed demonstrating a benefit of artificial intelligence–augmented endoscopy. Deep learning applied to small bowel capsule endoscopy also appears to enhance pathology detection and reduce capsule reading time. Prospective evaluation including the first randomised trial has been performed in the colon, demonstrating improved polyp and adenoma detection rates; however, these appear to be relevant to small polyps. There are potential additional roles of artificial intelligence relevant to improving the quality of endoscopic examinations, training and triaging of referrals. Further large-scale, multicentre and cross-platform validation studies are required for the robust incorporation of artificial intelligence–augmented diagnostic luminal endoscopy into our routine clinical practice. [ABSTRACT FROM AUTHOR]