Endoscopic ultrasound (EUS) has emerged as a widely utilized tool in the diagnosis of digestive diseases. In recent years, the potential of artificial intelligence (AI) in healthcare has been gradually recognized, and its superiority in the field of EUS is becoming apparent. Machine learning (ML) and deep learning (DL) are the two main AI algorithms. This paper aims to outline the applications and prospects of artificial intelligence-assisted endoscopic ultrasound (EUS-AI) in digestive diseases over the past decade. The results demonstrated that EUS-AI has shown superiority or at least equivalence to traditional methods in the diagnosis, prognosis, and quality control of subepithelial lesions, early esophageal cancer, early gastric cancer, and pancreatic diseases including pancreatic cystic lesions, autoimmune pancreatitis, and pancreatic cancer. The implementation of EUS-AI has opened up new avenues for individualized precision medicine and has introduced novel diagnostic and treatment approaches for digestive diseases.

There is dramatically increased incidence of several liver diseases worldwide; thus, an unmet need to diagnose and stage these pathological entities heralds the wide application of liver biopsy (LB) techniques. The ways of LB are versatile, including percutaneous LB, transjugular LB, and more recently an approach of minimal invasiveness, that is, EUS-guided LB (EUS-LB). In this review article, we come to the conclusion that EUS-LB may serve as a feasible, reliable, and safe alternative to percutaneous LB and transjugular LB in terms of improved diagnostic yield, excellent sampling performance, and controlled adverse events among patients with focal, infiltrative, and parenchymal liver diseases. Furthermore, extensive efforts have been made to optimize and refine several technical pillars within EUS-LB modality such as the selection of needle size/type, priming manner of biopsy needle, and choice of pass/actuation technique, all of which aim at obtaining better specimen quantity and quality. Another advantageous aspect and unique property pertinent to EUS-guided modality indicate that multiple screening, surveillance, and intervention procedures can be combined into one single endoscopic session. Accordingly, some pilot studies have clarified the clinical usefulness by integrating EUS-LB with simultaneous measurement of portal pressure gradient or examination of liver stiffness. However, more studies, in particular, randomized controlled trials or real-world evidence, are practically warranted to elucidate the validity and safety of EUS-LB as a regular/routine part of managing liver diseases.

At present, artificial intelligence (AI) has shown significant potential in digestive endoscopy image analysis, serving as a powerful auxiliary tool for the accurate diagnosis and treatment of gastrointestinal diseases. However, mainstream models represented by deep learning are often characterized as complex “black boxes,” with decision-making processes that are difficult for humans to interpret. The lack of interpretability undermines physicians’ trust in model results and hinders the broader use of models in clinical practice. To address this core challenge, Explainable AI (XAI) has emerged to enhance the transparency of decision-making, thereby establishing a foundation of trust for human–machine collaboration. The review systematically reviews 34 articles (7 articles in esophagogastroduodenoscopy, 13 articles in colonoscopy, 9 articles in endoscopic ultrasonography, and 5 articles in wireless capsule endoscopy), focusing on the research progress and applications of XAI in the field of digestive endoscopic image analysis, with particular emphasis on the visual explanation-based methods. We first clarify the definition and mainstream classification of XAI, then introduce the principles and characteristics of key XAI methods based on visual explanation. Subsequently, we review the applications of these methods in digestive endoscopy image analysis. Lastly, we explore the obstacles presently faced in this domain and the future directions. This study provides a theoretical basis for constructing a trustworthy and transparent AI-assisted digestive endoscopy diagnosis and treatment system and promotes the implementation and application of XAI in clinical practice.

Background: Large language models (LLMs) have shown potential for clinical decision support. However, the diagnostic performance of newly released frontier models—including GPT-5 and Gemini 2.5 Pro—in gastroenterology remains insufficiently evaluated. This study evaluated the diagnostic performance and repeated-run consistency of four frontier LLMs—DeepSeek-V3.1, GPT-5, GPT-4.1, and Gemini 2.5 Pro—in challenging gastroenterology cases. Methods: This cross-sectional comparative study evaluated four LLMs (DeepSeek-V3.1, GPT-5, GPT-4.1, and Gemini 2.5 Pro) using 50 challenging gastroenterology cases retrieved from PubMed. Cases published after February 2025 were selected to minimize the potential risk of training-data contamination and prior model exposure. Each case was structured into a standardized prompt comprising clinical history, examination findings, laboratory results, and imaging data. Three independent runs were performed for each case. First-run outputs were used to assess ordinal primary diagnostic scores and differential-diagnosis quality, whereas all three runs were used to assess repeated-run consistency. Outputs were anonymized and scored by a gastroenterologist blinded to model identity. Results: Gemini 2.5 Pro achieved significantly higher ordinal primary diagnostic scores than the other three models (all Holm-adjusted pairwise p < 0.01). Exact-match rates were 46% for Gemini 2.5 Pro, 22% for GPT-5, and 18% each for GPT-4.1 and DeepSeek-V3.1. All four models had a median differential-diagnosis quality score of 4; the Friedman test did not detect a statistically significant difference among the models (p = 0.725). Complete agreement in the primary diagnosis across all three runs was observed in 68–78% of cases, depending on the model. No statistically significant differences in repeated-run consistency scores were detected among the models. Conclusions: In this investigator-selected benchmark of 50 published gastroenterology cases, Gemini 2.5 Pro achieved higher ordinal primary diagnostic scores than the other three models. No statistically significant differences were detected in differential-diagnosis quality or repeated-run consistency. These nonsignificant findings do not establish equivalence. Prospective evaluation in clinical settings is required before drawing conclusions about clinical effectiveness.