Big data and variceal rebleeding prediction in cirrhosis patients

Big data has convincing merits in developing risk stratification strategies for diseases. The 6 “V”s of big data, namely, volume, velocity, variety, veracity, value, and variability, have shown promise for real-world scenarios. Big data can be applied to analyze health data and advance research in preclinical biology, medicine, and especially disease initiation, development, and control. A study design comprises data selection, inclusion and exclusion criteria, standard confirmation and cohort establishment, follow-up strategy, and events of interest. The development and efficiency verification of a prognosis model consists of deciding the data source, taking previous models as references while selecting candidate predictors, assessing model performance, choosing appropriate statistical methods, and model optimization. The model should be able to inform disease development and outcomes, such as predicting variceal rebleeding in patients with cirrhosis. Our work has merits beyond those of other colleagues with respect to cirrhosis patient screening and data source regarding variceal bleeding.

Artificial intelligence in pathological evaluation of gastrointestinal cancers

The integration of artificial intelligence (AI) has shown promising benefits in many fields of diagnostic histopathology, including for gastrointestinal cancers (GCs), such as tumor identification, classification, and prognosis prediction. In parallel, recent evidence suggests that AI may help reduce the workload in gastrointestinal pathology by automatically detecting tumor tissues and evaluating prognostic parameters. In addition, AI seems to be an attractive tool for biomarker/genetic alteration prediction in GC, as it can contain a massive amount of information from visual data that is complex and partially understandable by pathologists. From this point of view, it is suggested that advances in AI could lead to revolutionary changes in many fields of pathology. Unfortunately, these findings do not exclude the possibility that there are still many hurdles to overcome before AI applications can be safely and effectively applied in actual pathology practice. These include a broad spectrum of challenges from needs identification to cost-effectiveness. Therefore, unlike other disciplines of medicine, no histopathology-based AI application, including in GC, has ever been approved either by a regulatory authority or approved for public reimbursement. The purpose of this review is to present data related to the applications of AI in pathology practice in GC and present the challenges that need to be overcome for their implementation.

Diagnostic advances of artificial intelligence and radiomics in gastroenterology

Traditional medical imaging, including ultrasound, computed tomography, magnetic resonance imaging, or positron emission tomography, remains widely used diagnostic modalities for gastrointestinal diseases at present. These modalities are used to assess changes in morphology, attenuation, signal intensity, and enhancement characteristics. Gastrointestinal tumors, especially malignant tumors, are commonly seen in clinical practice with an increasing number of deaths each year. Because the imaging manifestations of different diseases usually overlap, accurate early diagnosis of tumor lesions, noninvasive and effective evaluation of tumor staging, and prediction of prognosis remain challenging. Fortunately, traditional medical images contain a great deal of important information that cannot be recognized by human eyes but can be extracted by artificial intelligence (AI) technology, which can quantitatively assess the heterogeneity of lesions and provide valuable information, including therapeutic effects and patient prognosis. With the development of computer technology, the combination of medical imaging and AI technology is considered to represent a promising field in medical image analysis. This new emerging field is called “radiomics”, which makes big data mining and extraction from medical imagery possible and can help clinicians make effective decisions and develop personalized treatment plans. Recently, AI and radiomics have been gradually applied to lesion detection, qualitative and quantitative diagnosis, histopathological grading and staging of tumors, therapeutic efficacy assessment, and prognosis evaluation. In this minireview, we briefly introduce the basic principles and technology of radiomics. Then, we review the research and application of AI and radiomics in gastrointestinal diseases, especially diagnostic advancements of radiomics in the differential diagnosis, treatment option, assessment of therapeutic efficacy, and prognosis evaluation of esophageal, gastric, hepatic, pancreatic, and colorectal diseases.

Dietary counseling based on artificial intelligence for patients with nonalcoholic fatty liver disease

BACKGROUND

About 25% of the general population in Japan are reported to have nonalcoholic fatty liver disease (NAFLD). NAFLD and nonalcoholic steatohepatitis carry a risk of progressing further to hepatocellular carcinoma. The primary treatment for NAFLD is dietary therapy. Dietary counseling plays an essential role in dietary therapy. Although artificial intelligence (AI)-based nutrition management software applications have been developed and put into practical use in recent years, the majority focus on weight loss or muscle strengthening, and no software has been developed for patient use in clinical practice.

AIM

To examine whether effective dietary counseling is possible using AI-based nutrition management software.

METHODS

NAFLD patients who had been assessed using an AI-based nutrition management software application (Calomeal) that automatically analyzed images of meals photographed by patients and agreed to receive dietary counseling were given dietary counseling. Blood biochemistry tests were performed before (baseline) and 6 mo after (6M follow-up) dietary counseling. After the dietary counseling, the patients were asked to complete a questionnaire survey.

RESULTS

A total of 29 patients diagnosed with NAFLD between August 2020 and March 2022 were included. There were significant decreases in liver enzyme and triglyceride levels at the 6M follow-up compared to baseline. The food analysis capability of the AI used by Calomeal in this study was 75.1%. Patient satisfaction with the AI-based dietary counselling was high.

CONCLUSION

AI-based nutrition management appeared to raise awareness of dietary habits among NAFLD patients. However, it did not directly alleviate the burden of registered dietitians, and improvements are much anticipated.

Risk factor profiles for gastric cancer prediction with respect to Helicobacter pylori: A study of a tertiary care hospital in Pakistan

BACKGROUND

Gastric cancer (GC) is the fourth leading cause of cancer-related deaths worldwide. Diagnosis relies on histopathology and the number of endoscopies is increasing. Helicobacter pylori (H. pylori) infection is a major risk factor.

AIM

To develop an in-silico GC prediction model to reduce the number of diagnostic surgical procedures. The meta-data of patients with gastroduodenal symptoms, risk factors associated with GC, and H. pylori infection status from Holy Family Hospital Rawalpindi, Pakistan, were used with machine learning.

METHODS

A cohort of 341 patients was divided into three groups [normal gastric mucosa (NGM), gastroduodenal diseases (GDD), and GC]. Information associated with socioeconomic and demographic conditions and GC risk factors was collected using a questionnaire. H. pylori infection status was determined based on urea breath test. The association of these factors and histopathological grades was assessed statistically. K-Nearest Neighbors and Random Forest (RF) machine learning models were tested.

RESULTS

This study reported an overall frequency of 64.2% (219/341) of H. pylori infection among enrolled subjects. It was higher in GC (74.2%, 23/31) as compared to NGM and GDD and higher in males (54.3%, 119/219) as compared to females. More abdominal pain (72.4%, 247/341) was observed than other clinical symptoms including vomiting, bloating, acid reflux and heartburn. The majority of the GC patients experienced symptoms of vomiting (91%, 20/22) with abdominal pain (100%, 22/22). The multinomial logistic regression model was statistically significant and correctly classified 80% of the GDD/GC cases. Age, income level, vomiting, bloating and medication had significant association with GDD and GC. A dynamic RF GC-predictive model was developed, which achieved > 80% test accuracy.

CONCLUSION

GC risk factors were incorporated into a computer model to predict the likelihood of developing GC with high sensitivity and specificity. The model is dynamic and will be further improved and validated by including new data in future research studies. Its use may reduce unnecessary endoscopic procedures. It is freely available.

Application of artificial intelligence in the prediction of immunotherapy efficacy in hepatocellular carcinoma: Current status and prospects

Artificial Intelligence (AI) has increased as a potent tool in medicine, with promising oncology applications. The emergence of immunotherapy has transformed the treatment terrain for hepatocellular carcinoma (HCC), offering new hope to patients with this challenging malignancy. This article examines the role and future of AI in forecasting the effectiveness of immunotherapy in HCC. We highlight the potential of AI to revolutionize the prediction of therapy response, thus improving patient selection and clinical outcomes. The article further outlines the challenges and future research directions in this emerging field.

Evaluating the accuracy and reproducibility of ChatGPT-4 in answering patient questions related to small intestinal bacterial overgrowth

BACKGROUND

Small intestinal bacterial overgrowth (SIBO) poses diagnostic and treatment challenges due to its complex management and evolving guidelines. Patients often seek online information related to their health, prompting interest in large language models, like GPT-4, as potential sources of patient education.

AIM

To investigate ChatGPT-4's accuracy and reproducibility in responding to patient questions related to SIBO.

METHODS

A total of 27 patient questions related to SIBO were curated from professional societies, Facebook groups, and Reddit threads. Each question was entered into GPT-4 twice on separate days to examine reproducibility of accuracy on separate occasions. GPT-4 generated responses were independently evaluated for accuracy and reproducibility by two motility fellowship-trained gastroenterologists. A third senior fellowship-trained gastroenterologist resolved disagreements. Accuracy of responses were graded using the scale: (1) Comprehensive; (2) Correct but inadequate; (3) Some correct and some incorrect; or (4) Completely incorrect. Two responses were generated for every question to evaluate reproducibility in accuracy.

RESULTS

In evaluating GPT-4's effectiveness at answering SIBO-related questions, it provided responses with correct information to 18/27 (66.7%) of questions, with 16/27 (59.3%) of responses graded as comprehensive and 2/27 (7.4%) responses graded as correct but inadequate. The model provided responses with incorrect information to 9/27 (33.3%) of questions, with 4/27 (14.8%) of responses graded as completely incorrect and 5/27 (18.5%) of responses graded as mixed correct and incorrect data. Accuracy varied by question category, with questions related to “basic knowledge” achieving the highest proportion of comprehensive responses (90%) and no incorrect responses. On the other hand, the “treatment” related questions yielded the lowest proportion of comprehensive responses (33.3%) and highest percent of completely incorrect responses (33.3%). A total of 77.8% of questions yielded reproducible responses.

CONCLUSION

Though GPT-4 shows promise as a supplementary tool for SIBO-related patient education, the model requires further refinement and validation in subsequent iterations prior to its integration into patient care.

Scope and caveats: Artificial intelligence in gastroenterology

The use of Artificial intelligence (AI) has evolved from its mid-20^th century origins to playing a pivotal tool in modern medicine. It leverages digital data and computational hardware for diverse applications, including diagnosis, prognosis, and treatment responses in gastrointestinal and hepatic conditions. AI has had an impact in diagnostic techniques, particularly endoscopy, ultrasound, and histopathology. AI encompasses machine learning, natural language processing, and robotics, with machine learning being central. This involves sophisticated algorithms capable of managing complex datasets, far surpassing traditional statistical methods. These algorithms, both supervised and unsupervised, are integral for interpreting large datasets. In liver diseases, AI's non-invasive diagnostic applications, particularly in non-alcoholic fatty liver disease, and its role in characterizing hepatic lesions is promising. AI aids in distinguishing between normal and cirrhotic livers and improves the accuracy of lesion characterization and prognostication of hepatocellular carcinoma. AI enhances lesion identification during endoscopy, showing potential in the diagnosis and management of early-stage esophageal carcinoma. In peptic ulcer disease, AI technologies influence patient management strategies. AI is useful in colonoscopy, particularly in detecting smaller colonic polyps. However, its applicability in non-academic settings requires further validation. Addressing these issues is vital for harnessing the potential of AI. In conclusion, while AI offers transformative possibilities in gastroenterology, careful integration and balancing of technical possibilities with ethical and practical application, is essential for optimal use.

Use of artificial intelligence in total mesorectal excision in rectal cancer surgery: State of the art and perspectives

BACKGROUND

Colorectal cancer is a major public health problem, with 1.9 million new cases and 953000 deaths worldwide in 2020. Total mesorectal excision (TME) is the standard of care for the treatment of rectal cancer and is crucial to prevent local recurrence, but it is a technically challenging surgery. The use of artificial intelligence (AI) could help improve the performance and safety of TME surgery.

AIM

To review the literature on the use of AI and machine learning in rectal surgery and potential future developments.

METHODS

Online scientific databases were searched for articles on the use of AI in rectal cancer surgery between 2020 and 2023.

RESULTS

The literature search yielded 876 results, and only 13 studies were selected for review. The use of AI in rectal cancer surgery and specifically in TME is a rapidly evolving field. There are a number of different AI algorithms that have been developed for use in TME, including algorithms for instrument detection, anatomical structure identification, and image-guided navigation systems.

CONCLUSION

AI has the potential to revolutionize TME surgery by providing real-time surgical guidance, preventing complications, and improving training. However, further research is needed to fully understand the benefits and risks of AI in TME surgery.

Role of artificial intelligence in Barrett’s esophagus

The application of artificial intelligence (AI) in gastrointestinal endoscopy has gained significant traction over the last decade. One of the more recent applications of AI in this field includes the detection of dysplasia and cancer in Barrett’s esophagus (BE). AI using deep learning methods has shown promise as an adjunct to the endoscopist in detecting dysplasia and cancer. Apart from visual detection and diagnosis, AI may also aid in reducing the considerable interobserver variability in identifying and distinguishing dysplasia on whole slide images from digitized BE histology slides. This review aims to provide a comprehensive summary of the key studies thus far as well as providing an insight into the future role of AI in Barrett’s esophagus.

Artificial intelligence applications in predicting the behavior of gastrointestinal cancers in pathology

Recent research has provided a wealth of data supporting the application of artificial intelligence (AI)-based applications in routine pathology practice. Indeed, it is clear that these methods can significantly support an accurate and rapid diagnosis by eliminating errors, increasing reliability, and improving workflow. In addition, the effectiveness of AI in the pathological evaluation of prognostic parameters associated with behavior, course, and treatment in many types of tumors has also been noted. Regarding gastrointestinal system (GIS) cancers, the contribution of AI methods to pathological diagnosis has been investigated in many studies. On the other hand, studies focusing on AI applications in evaluating parameters to determine tumor behavior are relatively few. For this purpose, the potential of AI models has been studied over a broad spectrum, from tumor subtyping to the identification of new digital biomarkers. The capacity of AI to infer genetic alterations of cancer tissues from digital slides has been demonstrated. Although current data suggest the merit of AI-based approaches in assessing tumor behavior in GIS cancers, a wide range of challenges still need to be solved, from laboratory infrastructure to improving the robustness of algorithms, before incorporating AI applications into real-life GIS pathology practice. This review aims to present data from AI applications in evaluating pathological parameters related to the behavior of GIS cancer with an overview of the opportunities and challenges encountered in implementing AI in pathology.

Artificial intelligence for early prediction of alcohol-related liver disease: Advances, challenges, and clinical applications

Alcohol-related liver disease (ARLD) remains a major public health concern, often diagnosed at advanced stages with limited treatment options. Early identification of high-risk individuals is crucial for timely intervention and improved patient outcomes. Artificial intelligence (AI) has emerged as a powerful tool for predicting ARLD, leveraging multi-omics data, machine learning algorithms, and non-invasive biomarkers. This review explores the current advancements in AI-driven ARLD prediction, highlighting key methodologies such as multi-omics data integration, gut microbiome-based modeling, and predictive analytics using machine learning techniques. AI models incorporating transcriptomics, proteomics, and clinical data have demonstrated high diagnostic accuracy, with some achieving an area under the curve exceeding 0.90. Furthermore, non-invasive biomarkers, including liver stiffness measurements and circulating proteomic panels, have been successfully integrated into AI frameworks for early detection and risk stratification. Despite these advancements, challenges such as data heterogeneity, model generalizability, and ethical considerations remain. Future directions include the development of advanced biomarker discovery, wearable and point-of-care AI-integrated technologies, and precision medicine approaches tailored to individual risk profiles. AI-driven models hold significant potential in transforming ARLD prediction and management, ultimately contributing to early diagnosis and improved clinical outcomes.

Machine learning better predicts colonoscopy duration

BACKGROUND

The use of machine learning (ML) to predict colonoscopy procedure duration has not been examined.

AIM

To assess if ML and data available at the time a colonoscopy procedure is scheduled could be used to estimate procedure duration more accurately than the current practice.

METHODS

Total 40168 colonoscopies from the Clinical Outcomes Research Initiative database were collected. ML models predicting procedure duration were developed using data available at time of scheduling. The top performing model was compared against historical practice. Models were evaluated based on accuracy (prediction – actual time) ± 5, 10, and 15 min.

RESULTS

ML outperformed historical practice with 77.1% to 68.9%, 87.3% to 79.6%, and 92.1% to 86.8% accuracy at 5, 10 and 15 min thresholds.

CONCLUSION

The use of ML to estimate colonoscopy procedure duration may lead to more accurate scheduling.

Artificial intelligence using advanced imaging techniques and cholangiocarcinoma: Recent advances and future direction

While cholangiocarcinoma represents only about 3% of all gastrointestinal tumors, it has a dismal survival rate, usually because it is diagnosed at a late stage. The utilization of Artificial Intelligence (AI) in medicine in general, and in gastroenterology has made gigantic steps. However, the application of AI for biliary disease, in particular for cholangiocarcinoma, has been sub-optimal. The use of AI in combination with clinical data, cross-sectional imaging (computed tomography, magnetic resonance imaging) and endoscopy (endoscopic ultrasound and cholangioscopy) has the potential to significantly improve early diagnosis and the choice of optimal therapeutic options, leading to a transformation in the prognosis of this feared disease. In this review we summarize the current knowledge on the use of AI for the diagnosis and management of cholangiocarcinoma and point to future directions in the field.

Implications of artificial intelligence in inflammatory bowel disease: Diagnosis, prognosis and treatment follow up

Driven by the tremendous availability of data, artificial intelligence (AI) using deep learning has emerged as a breakthrough computer technology in the last few decades and has recently been acknowledged by the Task Force on AI as a golden opportunity for research. With its ability to understand, learn from and build on non-linear relationships, AI aims to individualize medical care in an attempt to save time, cost, effort and improve patient’s safety. AI has been applied in multiple medical fields with substantial progress made in gastroenterology mainly to facilitate accurate detection of pathology in different disease processes, among which inflammatory bowel disease (IBD) seems to drag significant attention, specifically by interpreting imaging studies, endoscopic images and videos and -to a lesser extent- disease genomics. Moreover, models have been built to predict IBD occurrence, flare ups, persistence of histological inflammation, disease-related structural abnormalities as well as disease remission. In this article, we will review the applications of AI in IBD in the present medical literature at multiple points of IBD timeline, starting from disease prediction via genomic assessment, diagnostic phase via interpretation of radiological studies and AI-assisted endoscopy, and the role of AI in the evaluation of therapy response and prognosis of IBD patients.

Revolutionizing hepatobiliary surgery: Impact of three-dimensional imaging and virtual surgical planning on precision, complications, and patient outcomes

BACKGROUND

Hepatobiliary surgery is complex and requires a thorough understanding of the liver’s anatomy, biliary system, and vasculature. Traditional imaging methods such as computed tomography (CT) and magnetic resonance imaging (MRI), although helpful, fail to provide three-dimensional (3D) relationships of these structures, which are critical for planning and executing complicated surgeries.

AIM

To explore the use of 3D imaging and virtual surgical planning (VSP) technologies to improve surgical accuracy, reduce complications, and enhance patient recovery in hepatobiliary surgeries.

METHODS

A comprehensive review of studies published between 2017 and 2024 was conducted through PubMed, Scopus, Google Scholar, and Web of Science. Studies selected focused on 3D imaging and VSP applications in hepatobiliary surgery, assessing surgical precision, complications, and patient outcomes. Thirty studies, including randomized controlled trials, cohort studies, and case reports, were included in the final analysis.

RESULTS

Various 3D imaging modalities, including multidetector CT, MRI, and 3D rotational angiography, provide high-resolution views of the liver’s vascular and biliary anatomy. VSP allows surgeons to simulate complex surgeries, improving preoperative planning and reducing complications like bleeding and bile leaks. Several studies have demonstrated improved surgical precision, reduced complications, and faster recovery times when 3D imaging and VSP were used in complex surgeries.

CONCLUSION

3D imaging and VSP technologies significantly enhance the accuracy and outcomes of hepatobiliary surgeries by providing individualized preoperative planning. While promising, further research, particularly randomized controlled trials, is needed to standardize protocols and evaluate long-term efficacy.

Advancing the diagnosis and treatment of metabolic-associated steatotic liver disease: The transformative role of artificial intelligence

Metabolic-associated steatotic liver disease (MASLD), formerly referred to as non-alcoholic fatty liver disease, represents an escalating worldwide medical burden defined by hepatic steatosis, inflammation, fibrosis, and potential progression to cirrhosis or hepatocellular carcinoma. Artificial intelligence (AI) has emerged as a transformative tool in MASLD management, enhancing diagnostic accuracy, risk stratification, and treatment optimization. This review explores the integration of AI in MASLD diagnosis, including AI-based histopathological assessment, non-invasive screening models, imaging diagnostics, and gut microbiota-based approaches. Additionally, AI-driven treatment strategies facilitate personalized management, assess therapeutic response, and contribute to drug discovery. Despite its advantages, challenges such as data integration, model interpretability, and cost-effectiveness remain obstacles to widespread adoption. Future advancements in explainable AI, multi-modal data fusion, and cost-efficient implementations will be crucial for maximizing AI’s impact on MASLD care. AI-driven innovations hold great promise for improving early detection, guiding personalized treatment, and ultimately enhancing patient outcomes in MASLD.

Clinical use of augmented reality, mixed reality, three-dimensional-navigation and artificial intelligence in liver surgery

A precise knowledge of intra-parenchymal vascular and biliary architecture and the location of lesions in relation to the complex anatomy is indispensable to perform liver surgery. Therefore, virtual three-dimensional (3D)-reconstruction models from computed tomography/magnetic resonance imaging scans of the liver might be helpful for visualization. Augmented reality, mixed reality and 3D-navigation could transfer such 3D-image data directly into the operation theater to support the surgeon. This review examines the literature about the clinical and intraoperative use of these image guidance techniques in liver surgery and provides the reader with the opportunity to learn about these techniques. Augmented reality and mixed reality have been shown to be feasible for the use in open and minimally invasive liver surgery. 3D-navigation facilitated targeting of intraparenchymal lesions. The existing data is limited to small cohorts and description about technical details e.g., accordance between the virtual 3D-model and the real liver anatomy. Randomized controlled trials regarding clinical data or oncological outcome are not available. Up to now there is no intraoperative application of artificial intelligence in liver surgery. The usability of all these sophisticated image guidance tools has still not reached the grade of immersion which would be necessary for a widespread use in the daily surgical routine. Although there are many challenges, augmented reality, mixed reality, 3D-navigation and artificial intelligence are emerging fields in hepato-biliary surgery.

Role of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma

Artificial intelligence (AI) evolved many years ago, but it gained much advancement in recent years for its use in the medical domain. AI with its different subsidiaries, i.e. deep learning and machine learning, examine a large amount of data and performs an essential part in decision-making in addition to conquering the limitations related to human evaluation. Deep learning tries to imitate the functioning of the human brain. It utilizes much more data and intricate algorithms. Machine learning is AI based on automated learning. It utilizes earlier given data and uses algorithms to arrange and identify models. Globally, hepatocellular carcinoma is a major cause of illness and fatality. Although with substantial progress in the whole treatment strategy for hepatocellular carcinoma, managing it is still a major issue. AI in the area of gastroenterology, especially in hepatology, is particularly useful for various investigations of hepatocellular carcinoma because it is a commonly found tumor, and has specific radiological features that enable diagnostic procedures without the requirement of the histological study. However, interpreting and analyzing the resulting images is not always easy due to change of images throughout the disease process. Further, the prognostic process and response to the treatment process could be influenced by numerous components. Currently, AI is utilized in order to diagnose, curative and prediction goals. Future investigations are essential to prevent likely bias, which might subsequently influence the analysis of images and therefore restrict the consent and utilization of such models in medical practices. Moreover, experts are required to realize the real utility of such approaches, along with their associated potencies and constraints.

Liver surgery for colorectal metastasis: New paths and new goals with the help of artificial intelligence

Colorectal cancer is one of the most common neoplasia with an high risk to metastatic spread. Improving medical and surgical treatment is moving along with improving the precision of diagnosis and patient's assessment, the latter two aided more and more with the use of artificial intelligence (AI). The management of colorectal liver metastasis is multidisciplinary, and surgery is the main option. After the diagnosis, a surgical assessment of the patient is fundamental. Reaching a R0 resection with a proper remnant liver volume can be done using new techniques involving also artificial intelligence. Considering the recent application of artificial intelligence as a valid substitute for liver biopsy in chronic liver diseases, several authors tried to apply similar techniques to pre-operative imaging of liver metastasis. Radiomics showed good results in identifying structural changes in a unhealthy liver and in evaluating the prognosis after a liver resection. Recently deep learning has been successfully applied in estimating the remnant liver volume before surgery. Moreover AI techniques can help surgeons to perform an early diagnosis of neoplastic relapse or a better differentiation between a colorectal metastasis and a benign lesion. AI could be applied also in the histopathological diagnostic tool. Although AI implementation is still partially automatized, it appears faster and more precise than the usual diagnostic tools and, in the short future, could become the new gold standard in liver surgery.

Artificial intelligence in gastroenterology: A narrative review

Artificial intelligence (AI) is a complex concept, broadly defined in medicine as the development of computer systems to perform tasks that require human intelligence. It has the capacity to revolutionize medicine by increasing efficiency, expediting data and image analysis and identifying patterns, trends and associations in large datasets. Within gastroenterology, recent research efforts have focused on using AI in esophagogastroduodenoscopy, wireless capsule endoscopy (WCE) and colonoscopy to assist in diagnosis, disease monitoring, lesion detection and therapeutic intervention. The main objective of this narrative review is to provide a comprehensive overview of the research being performed within gastroenterology on AI in esophagogastroduodenoscopy, WCE and colonoscopy.