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September 10, 2025, 17:34

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With the aid of deep learning, specifically the convolutional neural network, the paradigm of operator dependent endoscopy shifts towards operator independence, being one of the most important/impactful aspects of AI – assisted endoscopic analysis that this minireview focuses on. As stated in this review, there are 3 most common used models of machine learning in clinical applications. So, in order for them to perform at its’ highest capacity, one must master the ability of choosing between them for the clinical applications. Regarding image enhancement, algorithms such as GANs, U-Net variants and DnCNN, have revolutionized endoscopy. Moreover, the shift from 2D to 3D reconstruction technology, allows for on-site evaluations of disease status, therefore providing and enhanced clinical diagnosis. A probably more impactful aid AI provided in endoscopy, is the early diagnosis of malignant tumors, decrease the rate of missed diagnoses, specifically gastric cancer. Trials conducted by Shi et al (1), Arai et al (2), and Chen et al (3), reported remarkable sensitivity and specifity integrated with risk factors, thus providing providing personalized follow up strategies post upper endoscopy. However, regarding both detecting and classifying colon polyps, Namikawa et al revealed that AI showed superior sensitivity, outperforming experts, albeit with slighty less specificity in a study comparing the capabilities of AI against human experts. Having that in mind, a thorough evaluation and then integration of AI in the decision making process algorithm. As for non malignant diseases, the most importanat challenge is data heterogeneity and insufficient clinical validation. However, the application of machine learning in inflammatory bowel disease (IBD) shows promise in objective grading of IBD, in reshaping the monitoring paradigm, converging into an “active early warning” system. Moreover, AI models, benefit the IBD clinical trials, by improved endoscopy quality, consistent, valid, real -time assessment of IBD severity at site level, improved patient recruitment, increased sensitivity to responde and patient response to treatment (4). As with all important innovations, limitations arise, specifically data privacy and annotation quality that hinder model traninig. A study conducted by Buedgens et al (5) reported that weakly supervised AI systems can achieve a high performance and maintain explainability in end-to-end image analysis in GI endoscopy. This shows that manual annotations are not necessarily a bottleneck for future clinical applications of AI. Finally, in order to provide a much smoother clinical translation of machine learning, expert bodies, such as ASGE and ESGE have provided position statements as for priorities for artificial intelligence in GI endoscopy and expected value of artificial intelligence in gastrointestinal endoscopy, respectively (6,7), therefore offering standardization.

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