In the rapidly evolving landscape of gastrointestinal health care, the integration of artificial intelligence (AI) presents unprecedented opportunities for enhancing patient outcomes, improving efficiency, and driving innovation. Effective collaboration among clinicians, academia, and industry is crucial to harness the full potential of AI technologies. Clinicians offer invaluable insights from real-world practice, ensuring that AI solutions address genuine clinical needs and improve patient care. Academia plays a pivotal role in advancing research, developing new methodologies, and training the next generation of professionals who will navigate this transformative field. Industry drives the commercialization of AI tools, providing the resources and infrastructure necessary for widespread adoption. Achieving these synergies is challenging. Issues including data privacy, regulatory hurdles, and interdisciplinary communication must be addressed to foster effective partnerships. By embracing collaborative models, including public-private partnerships, clinical trials, and innovation hubs, stakeholders can work together to overcome barriers and promote responsible AI integration in gastroenterology.

Owing to the complex and often asymptomatic presentations, the diagnosis of biliopancreatic diseases, including pancreatic and biliary malignancies, remains challenging. Recent technological advancements have remarkably improved the diagnostic accuracy and patient outcomes in these diseases. This review explores key advancements in diagnostic modalities, including biomarkers, imaging techniques, and artificial intelligence (AI)-based technologies. Biomarkers, such as cancer antigen 19-9, KRAS mutations, and inflammatory markers, provide crucial insights into disease progression and treatment responses. Advanced imaging modalities include enhanced computed tomography (CT), positron emission tomography-CT, magnetic resonance cholangiopancreatography, and endoscopic ultrasound. AI integration in imaging and pathology has enhanced diagnostic precision through deep learning algorithms that analyze medical images, automate routine diagnostic tasks, and provide predictive analytics for personalized treatment strategies. The applications of these technologies are diverse, ranging from early cancer detection to therapeutic guidance and real-time imaging. Biomarker-based liquid biopsies and AI-assisted imaging tools are essential for non-invasive diagnostics and individualized patient management. Furthermore, AI-driven models are transforming disease stratification, thus enhancing risk assessment and decision-making. Future studies should explore standardizing biomarker validation, improving AI-driven diagnostics, and expanding the accessibility of advanced imaging technologies in resource-limited settings. The continued development of non-invasive diagnostic techniques and precision medicine approaches is crucial for optimizing the detection and management of biliopancreatic diseases. Collaborative efforts between clinicians, researchers, and industry stakeholders will be pivotal in applying these advancements in clinical practice.

The rising incidence of pancreatic diseases, including acute and chronic pancreatitis and various pancreatic neoplasms, poses a significant global health challenge. Pancreatic ductal adenocarcinoma (PDAC) for example, has a high mortality rate due to late-stage diagnosis and its inaccessible location. Advances in imaging technologies, though improving diagnostic capabilities, still necessitate biopsy confirmation. Artificial intelligence, particularly machine learning and deep learning, has emerged as a revolutionary force in healthcare, enhancing diagnostic precision and personalizing treatment. This narrative review explores Artificial intelligence's role in pancreatic imaging, its technological advancements, clinical applications, and associated challenges. Following the PRISMA-DTA guidelines, a comprehensive search of databases including PubMed, Scopus, and Cochrane Library was conducted, focusing on Artificial intelligence, machine learning, deep learning, and radiomics in pancreatic imaging. Articles involving human subjects, written in English, and published up to March 31, 2024, were included. The review process involved title and abstract screening, followed by full-text review and refinement based on relevance and novelty. Recent Artificial intelligence advancements have shown promise in detecting and diagnosing pancreatic diseases. Deep learning techniques, particularly convolutional neural networks (CNNs), have been effective in detecting and segmenting pancreatic tissues as well as differentiating between benign and malignant lesions. Deep learning algorithms have also been used to predict survival time, recurrence risk, and therapy response in pancreatic cancer patients. Radiomics approaches, extracting quantitative features from imaging modalities such as CT, MRI, and endoscopic ultrasound, have enhanced the accuracy of these deep learning models. Despite the potential of Artificial intelligence in pancreatic imaging, challenges such as legal and ethical considerations, algorithm transparency, and data security remain. This review underscores the transformative potential of Artificial intelligence in enhancing the diagnosis and treatment of pancreatic diseases, ultimately aiming to improve patient outcomes and survival rates.

The integration of Artificial Intelligence (AI) in endoscopic ultrasound (EUS) represents a transformative advancement in the early detection and management of biliopancreatic lesions. This review highlights the current state of AI-enhanced EUS (AI-EUS) for diagnosing solid and cystic pancreatic lesions, as well as biliary diseases. AI-driven models, including machine learning (ML) and deep learning (DL), have shown significant improvements in diagnostic accuracy, particularly in distinguishing pancreatic ductal adenocarcinoma (PDAC) from benign conditions and in the characterization of pancreatic cystic neoplasms. Advanced algorithms, such as convolutional neural networks (CNNs), enable precise image analysis, real-time lesion classification, and integration with clinical and genomic data for personalized care. In biliary diseases, AI-assisted systems enhance bile duct visualization and streamline diagnostic workflows, minimizing operator dependency. Emerging applications, such as AI-guided EUS fine-needle aspiration (FNA) and biopsy (FNB), improve diagnostic yields while reducing errors. Despite these advancements, challenges remain, including data standardization, model interpretability, and ethical concerns regarding data privacy. Future developments aim to integrate multimodal imaging, real-time procedural support, and predictive analytics to further refine the diagnostic and therapeutic potential of AI-EUS. AI-driven innovation in EUS stands poised to revolutionize pancreatico-biliary diagnostics, facilitating earlier detection, enhancing precision, and paving the way for personalized medicine in gastrointestinal oncology and beyond.

The incidence and prevalence of pancreatic neuroendocrine neoplasms are steadily increasing. These tumors are highly heterogeneous, with treatment options ranging from observation to surgery, and various medical therapies. The choice of treatment is influenced by factors such as tumor stage, grade (proliferative activity), and the presence of hormone-related syndromes. Endoscopic ultrasound (EUS) is becoming increasingly valuable for assessing pancreatic neuroendocrine neoplasms, offering detailed morphological, vascular, and functional information through techniques such as contrast enhancement and elastography. It also allows biopsies that are useful for both histopathological and molecular analyses. These tumors are highly heterogeneous, with treatment options ranging from observation to various medical therapies and surgery. Recent data suggest that small, non-functioning PanNENs with low proliferation rates may be safely monitored, whereas more aggressive or functioning tumors typically require surgery. EUS-guided ablation is a promising alternative for patients with functional pancreatic neuroendocrine neoplasms who are unsuitable for surgery, although randomized trials are needed. In non-resectable pancreatic neuroendocrine neoplasms, treatment options include somatostatin analogs, targeted therapies (e.g., everolimus, sunitinib), chemotherapy, and radioligand therapy. This review discusses key factors in planning personalized treatment strategies for pancreatic neuroendocrine neoplasms.

Pancreaticobiliary diseases can lead to significant morbidity and their diagnoses rely on imaging and endoscopy which are dependent on operator expertise. Artificial intelligence (AI) has seen a rapid uptake in the field of luminal endoscopy, such as polyp detection during colonoscopy. However, its use for pancreaticobiliary endoscopic modalities such as endoscopic ultrasound (EUS) and cholangioscopy remains scarce, with only few studies available. In this review, we delve into the current evidence, benefits, limitations, and future scope of AI technologies in pancreaticobiliary endoscopy.

Pancreatic cancer serves as the third leading cause of cancer-associated morbidity and mortality in the United States, with a 5-year survival rate of only 12% with an expected increase in incidence and mortality in the coming years. Pancreatic ductal adenocarcinomas constitute most pancreatic malignancies. Certain genetic syndromes, including Lynch syndrome, hereditary breast and ovarian cancer syndrome, hereditary pancreatitis, familial adenomatous polyposis, Peutz-Jeghers syndrome, familial pancreatic cancer mutation, and ataxia telangiectasia, confer a significantly higher risk. Screening for pancreatic malignancies currently targets patients with germline mutations or those with significant family history. Screening the general population is not currently viable owing to overall low incidence and lack of specific tests. Endoscopic ultrasound (EUS) and its applied advances are increasingly being used for surveillance, diagnosis, and management of pancreatic malignancies and have now become an indispensable tool in their management. For patients with risk factors, EUS in combination with magnetic resonance imaging/magnetic resonance cholangiopancreatography is used for screening. The role of endoscopic modalities has been expanding with the increased utilization of endoscopic retrograde cholangiopancreatography, EUS-directed therapies include EUS-guided fine-needle aspiration and EUS-fine-needle biopsy (FNB). EUS combined with FNB has the highest specificity and sensitivity for detecting pancreatic cancer amongst available modalities. Studies also recognize that artificial intelligence assisted EUS in the early detection of pancreatic cancer. At the same time, surgical resection has been historically considered the only curative treatment for pancreatic cancer, over 80% of patients present with unresectable disease. We also discuss EUS-guided therapies of physicochemicals (radiofrequency ablation, brachytherapy, and intratumor chemotherapy), biological agents (gene therapies and oncolytic viruses), and immunotherapy. We aim to perform a detailed review of the current burden, risk factors, role of screening, diagnosis, and endoscopic advances in the treatment modalities available for pancreatic cancer.

The pancreas is a vital organ in digestive system which has significant health implications. It is imperative to evaluate and identify malignant pancreatic lesions promptly in light of the high mortality rate linked to such malignancies. Endoscopic Ultrasound (EUS) is a non-invasive precise technique to detect pancreas disorders, but it is highly operator dependent. Artificial intelligence (AI), including traditional machine learning (ML) and deep learning (DL) techniques can play a pivotal role to enhancing the performance of EUS regardless of operator. AI performs a critical function in the detection, classification, and segmentation of medical images. The utilization of AI-assisted systems has improved the accuracy and productivity of pancreatic analysis, including the detection of diverse pancreatic disorders (e.g., pancreatitis, masses, and cysts) as well as landmarks and parenchyma. This systematic review examines the rapidly developing domain of AI-assisted system in EUS of the pancreas. Its objective is to present a thorough study of the present research status and developments in this area. This paper explores the significant challenges of AI-assisted system in pancreas EUS imaging, highlights the potential of AI techniques in addressing these challenges, and suggests the scope for future research in domain of AI-assisted EUS systems.

The emergence of endoscopic ultrasound (EUS) has significantly impacted the diagnosis and management of pancreatic cancer and its associated sequelae. While the definitive role of EUS for pancreatic cancer remains incompletely characterized by currently available guidelines, EUS undoubtedly offers high diagnostic accuracy, the precise staging of pancreatic neoplasms, and the ability to perform therapeutic and palliative interventions. However, current challenges to EUS include limited specialized expertise and variability in operator proficiency. As the technology and techniques continue to evolve and become more refined, EUS is poised to play an increasingly integral role in shaping pancreatic cancer care.

Pancreatic cancer is one of the leading causes of cancer-related deaths worldwide. Pancreatic lesions consist of both neoplastic and non-neoplastic lesions and often pose a diagnostic and therapeutic challenge due to similar clinical and radiological features. In recent years, pancreatic lesions have been discovered more frequently as incidental findings due to the increased utilization and widespread availability of abdominal cross-sectional imaging. Therefore, it becomes imperative to establish an early and appropriate diagnosis with meticulous differentiation in an attempt to balance unnecessary treatment of benign pancreatic lesions and missing the opportunity for early intervention in malignant lesions. Endoscopic ultrasound (EUS) has become an important diagnostic modality for the identification and risk stratification of pancreatic lesions due to its ability to provide detailed imaging and acquisition of tissue samples for analysis with the help of fine-needle aspiration/biopsy. The recent development of EUS-based technology, including contrast-enhanced endoscopic ultrasound, real-time elastography-endoscopic ultrasound, miniature probe ultrasound, confocal laser endomicroscopy, and the application of artificial intelligence has significantly augmented the diagnostic accuracy of EUS as it enables better evaluation of the number, location, dimension, wall thickness, and contents of these lesions. This article provides a comprehensive overview of the role of the different types of EUS available for the diagnosis and differentiation of pancreatic cancer from other pancreatic lesions while discussing their key strengths and important limitations.

Pancreatic ductal adenocarcinoma (PDAC) is the most common (90%) type of solid pancreatic neoplasm. Due to its late presentation and poor survival rate, early diagnosis and timely treatment is of utmost importance for better clinical outcomes. Endoscopic ultrasound provides high-resolution images of the pancreas and has excellent sensitivity in the diagnosis of even small (<2 cm) pancreatic lesions. Apart from imaging, it also has an advantage of tissue acquisition (EUS fine-needle aspiration, FNA; or fine-needle biopsy, FNB) for definitive diagnoses. EUS-guided tissue acquisition plays a crucial role in genomic and molecular studies, which in today's era of personalized medicine, are likely to become important components of PDAC management. With the use of better needle designs and technical advancements, EUS has now become an indispensable tool in the management of PDAC. Lastly, artificial intelligence for the detection of pancreatic lesions and newer automated needles for tissue acquisition will obviate observer dependency in the near future, resulting in the wider dissemination and adoption of this technology for improved outcomes in patients with PDAC.

Diagnosing pancreatic lesions, including chronic pancreatitis, autoimmune pancreatitis, and pancreatic cancer, poses a challenge and, as a result, is time-consuming. To tackle this issue, artificial intelligence (AI) has been increasingly utilized over the years. AI can analyze large data sets with heightened accuracy, reduce interobserver variability, and can standardize the interpretation of radiologic and histopathologic lesions. Therefore, this study aims to review the use of AI in the detection and differentiation of pancreatic space-occupying lesions and to compare AI-assisted endoscopic ultrasound (EUS) with conventional EUS in terms of their detection capabilities.

Literature searches were conducted through PubMed/Medline, SCOPUS, and Embase to identify studies eligible for inclusion. Original articles, including observational studies, randomized control trials, systematic reviews, meta-analyses, and case series specifically focused on AI-assisted EUS in adults, were included. Data were extracted and pooled, and a meta-analysis was conducted using Meta-xl. For results exhibiting significant heterogeneity, a random-effects model was employed; otherwise, a fixed-effects model was utilized.

A total of 21 studies were included in the review with four studies pooled for a meta-analysis. A pooled accuracy of 93.6% (CI 90.4-96.8%) was found using the random-effects model on four studies that showed significant heterogeneity ( P <0.05) in the Cochrane's Q test. Further, a pooled sensitivity of 93.9% (CI 92.4-95.3%) was found using a fixed-effects model on seven studies that showed no significant heterogeneity in the Cochrane's Q test. When it came to pooled specificity, a fixed-effects model was utilized in six studies that showed no significant heterogeneity in the Cochrane's Q test and determined as 93.1% (CI 90.7-95.4%). The pooled positive predictive value which was done using the random-effects model on six studies that showed significant heterogeneity was 91.6% (CI 87.3-95.8%). The pooled negative predictive value which was done using the random-effects model on six studies that showed significant heterogeneity was 93.6% (CI 90.4-96.8%).

AI-assisted EUS shows a high degree of accuracy in the detection and differentiation of pancreatic space-occupying lesions over conventional EUS. Its application may promote prompt and accurate diagnosis of pancreatic pathologies.

Predictive models (PMs) in endoscopic retrograde cholangiopancreatography (ERCP) and endoscopic ultrasound (EUS) have the potential to improve patient outcomes, enhance diagnostic accuracy, and guide therapeutic interventions. This review aims to summarize the current state of predictive models in ERCP and EUS and their clinical implications. To be considered useful in clinical practice a PM should be accurate, easy to perform, and may consider objective variables. PMs in ERCP estimate correct indication, probability of success, and the risk of developing adverse events. These models incorporate patient-related factors and technical aspects of the procedure. In the field of EUS, these models utilize clinical and imaging data to predict the likelihood of malignancy, presence of specific lesions, or risk of complications related to therapeutic interventions. Further research, validation, and refinement are necessary to maximize the utility and impact of these models in routine clinical practice.

The use of artificial intelligence (AI) in endoscopy has shown immense potential to enhance diagnostic accuracy, streamline procedures, and improve patient outcomes. There are potential uses in every field of endoscopy, from improving adenoma detection rate (ADR) in colonoscopy to reducing read time in capsule endoscopy or minimizing blind spots in gastroscopy. Indeed, some of these systems are already licensed and in commercial use across the world. In the Middle East, where healthcare systems are rapidly evolving, there is a growing interest in adopting AI technologies to revolutionise endoscopic practices. This article provides an overview of the advancements, potential opportunities and challenges associated with the implementation of AI in endoscopy within the Middle East region. Our aim is to contribute to the ongoing dialogue surrounding the implementation of AI in endoscopy and consider some of the factors that are particularly relevant in the Middle Eastern context, including the need to train the models for local populations, cost and training, as well as trying to ensure equity of access for patients. It provides valuable insights for healthcare professionals, policymakers, and researchers interested in leveraging AI to enhance endoscopic procedures, improve patient care, and address the unique healthcare needs of the Middle East population.

Solid pancreatic lesions (SPLs) encompass a variety of benign and malignant diseases and accurate diagnosis is crucial for guiding appropriate treatment decisions. Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) serves as a front-line diagnostic tool for pancreatic mass lesions and is widely used in clinical practice. Artificial intelligence (AI) is a mathematical technique that automates the learning and recognition of data patterns. Its strong self-learning ability and unbiased nature have led to its gradual adoption in the medical field. In this paper, we describe the fundamentals of AI and provide a summary of reports on AI in EUS-FNA/B to help endoscopists understand and realize its potential in improving pathological diagnosis and guiding targeted EUS-FNA/B. However, AI models have limitations and shortages that need to be addressed before clinical use. Furthermore, as most AI studies are retrospective, large-scale prospective clinical trials are necessary to evaluate their clinical usefulness accurately. Although AI in EUS-FNA/B is still in its infancy, the constant input of clinical data and the advancements in computer technology are expected to make computer-aided diagnosis and treatment more feasible.

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.

Endosonography, a minimally invasive imaging technique, has revolutionized the diagnosis and management of pancreatic diseases. This comprehensive review highlights the latest advancements in endosonography of the pancreas, focusing on key technological developments, procedural techniques, clinical applications and additional techniques, which include real-time elastography endoscopic ultrasound, contrast-enhanced-EUS, EUS-guided fine-needle aspiration or EUS-guided fine-needle biopsy. EUS is well established for T-staging and N-staging of pancreaticobiliary malignancies, for pancreatic cyst discovery, for identifying subepithelial lesions (SEL), for differentiation of benign pancreaticobiliary disorders or for acquisition of tissue by EUS-guided fine-needle aspiration or EUS-guided fine-needle biopsy. This review briefly describes principles and application of EUS and its related techniques.

Endoscopic ultrasound (EUS) provides high-resolution and real-time visualization of various layers of the gastrointestinal (GI) tract and beyond by combining ultrasound technology with endoscopic visualization [...].

Pancreatic cancer is a highly lethal disease with an aggressive clinical course. Patients with pancreatic cancer are usually asymptomatic until significant progression of their disease. Additionally, there are no effective screening guidelines for pancreatic cancer in the general population. This leads to a delay in diagnosis and treatment, resulting in poor clinical outcomes and low survival rates. Endoscopic Ultrasound (EUS) is an indispensable tool for the diagnosis and staging of pancreatic cancer. In the modern era, with exponential advancements in technology and device innovation, EUS is also being increasingly used in a variety of therapeutic interventions. In the context of pancreatic cancer where therapies are limited due to the advanced stage of the disease at diagnosis, EUS-guided interventions offer new and innovative options. Moreover, due to their minimally invasive nature and ability to provide real-time images for tumor localization and therapy, they are associated with fewer complication rates compared to conventional open and laparoscopic approaches. In this article, we detail the most current and important therapeutic applications of EUS for pancreatic cancer, namely EUS-guided Fine Needle Injections, EUS-guided Radiotherapy, and EUS-guided Ablations. Furthermore, we also discuss the feasibility and safety profile of each intervention in patients with pancreatic cancer to provide gastrointestinal medical oncologists, radiation and surgical oncologists, and therapeutic endoscopists with valuable information to facilitate patient discussions and aid in the complex decision-making process.