Artificial intelligence in endoscopy: Overview, applications, and future directions
Artificial intelligence (AI) is an umbrella term for a computer program with the ability to learn and solve novel problems that it has not previously encountered. AI has been increasingly utilized in medicine to aid in the diagnosis, treatment, and management of various diseases. The application of AI to endoscopy in gastroenterology is no exception, and it is in fact the leading field in the development of AI applications in medicine driven forward by a number of clinical trials. Development of AI systems in endoscopy The basic framework of AI development requires the input of a training set, which is a curated and confirmed set of data that is representative of the clinical question. Mathematical algorithms are “trained” on this dataset to recognize existing patterns and apply similar predictions to new datasets, thus answering questions about new situations without human input; this basic structure is termed machine learning. Deep learning is the subset of machine learning that furthers this computational ability by incorporating multiple mathematical algorithms, allowing it to address increasingly complex data with accuracy. A powerful deep learning model is the convolutional neural network (CNN). CNNs use a hierarchical arrangement of layered algorithms, which provide the ability to appreciate and learn from increasingly granular data features in a process that resembles the organization of the visual cortex, making it well suited for image recognition and video analysis . Regardless of the computational model used, the developed algorithms are then tested against independent validation and test datasets to evaluate the model’s performance. AI applications are set to revolutionize endoscopy with a growing number of studies and applications. In this review, we aim to 1) describe the different domains of AI, 2) summarize the major AI applications by anatomical location, and 3) discuss future directions and challenges for AI in endoscopy. 

The basic framework of AI development requires the input of a training set, which is a curated and confirmed set of data that is representative of the clinical question. Mathematical algorithms are “trained” on this dataset to recognize existing patterns and apply similar predictions to new datasets, thus answering questions about new situations without human input; this basic structure is termed machine learning. Deep learning is the subset of machine learning that furthers this computational ability by incorporating multiple mathematical algorithms, allowing it to address increasingly complex data with accuracy. A powerful deep learning model is the convolutional neural network (CNN). CNNs use a hierarchical arrangement of layered algorithms, which provide the ability to appreciate and learn from increasingly granular data features in a process that resembles the organization of the visual cortex, making it well suited for image recognition and video analysis . Regardless of the computational model used, the developed algorithms are then tested against independent validation and test datasets to evaluate the model’s performance. AI applications are set to revolutionize endoscopy with a growing number of studies and applications. In this review, we aim to 1) describe the different domains of AI, 2) summarize the major AI applications by anatomical location, and 3) discuss future directions and challenges for AI in endoscopy. 

Computer-aided detection, diagnosis, and quality assessment The paradigms of computer-aided detection (CADe), computer-aided diagnosis (CADx), and computer-aided quality assessment (CADq) organize AI applications into clinically meaningful categories. Broadly, CADe is used in the detection of an abnormality, whereas CADx characterizes abnormalities to predict a diagnosis. CADq uses AI to evaluate the quality of endoscopic procedures for the purpose of improving the accuracy and efficiency of diagnostic assessments as well as improving overall administration of the procedure. Below, we provide an overview of these domains , with specific applications discussed in detail in the following section. Computer-assisted detection (CADe) As management begins with the recognition of an abnormality, CADe systems were the first area of interest for AI applications in endoscopy. Mirroring clinical practice, the data input for CADe systems is typically from standard endoscopy techniques, such as white light (WL) endoscopy. The application of CADe is best illustrated with the detection of colonic polyps, which is also the most established area of AI in endoscopy. Early CADe studies began with the recognition of polyp features in still frames to produce a binary outcome of whether or not a lesion was detected. With technological advances, CADe systems have evolved to assist the operator in the detection of potential colonic polyps in real time [ , top panel]. Several CADe systems for polyps are now commercially available around the world, such as GI Genius and ENDO-AID. Similar principles have been applied to other gastrointestinal pathologies including Barrett’s esophagus detection, obscure GI bleeding, and Crohn’s disease lesions within the small bowel. Increasing complexity and CADx applications CADx systems provide a diagnosis by classifying lesions into known entities. As diagnosis often requires more characterization of features, these models may use contrast generating techniques (e.g., image-enhanced endoscopy—IEE) or high-magnification techniques (e.g., laser endomicroscopy and endocytoscopy). Thus, the output of CADx applications can be more complex and include grading, size, and characteristic elements normally only identifiable through ex vivo histopathological assessment. Similar to CADe, the most prominent example of CADx application remains the diagnosis of colorectal polyp histopathology, with several available commercial models (e.g., EndoBRAIN), and may be integrated with CADe models (e.g., CAD EYE). Combined CADe/CADx models provide a powerful workflow that can assist endoscopists in both the identification of colorectal neoplasia and the prediction of histopathology to inform management. Other CADx applications include gastric mucosa assessment and grading of inflammatory bowel diseases. Quality metrics and CADq applications Most recently, AI systems have been applied to quality assessment in endoscopy. These CADq systems have the most diverse reportable outcomes, which may include clinical metrics, such as adenoma detection rate (ADR), or systems outcomes such as healthcare costs . We propose a classification of CADq applications in endoscopy into “procedure assurance” and “procedure administration.” Procedure assurance applications focus on improving image quality and operator performance, such as bowel preparation assessment, monitoring for blind spots, and adequacy of mucosal exposure during endoscopy. Procedure administration applications standardize quality indicators and increase the efficiency of administering endoscopic procedures. These include automatic identification of landmarks with photo documentation and timing of procedures. 

The paradigms of computer-aided detection (CADe), computer-aided diagnosis (CADx), and computer-aided quality assessment (CADq) organize AI applications into clinically meaningful categories. Broadly, CADe is used in the detection of an abnormality, whereas CADx characterizes abnormalities to predict a diagnosis. CADq uses AI to evaluate the quality of endoscopic procedures for the purpose of improving the accuracy and efficiency of diagnostic assessments as well as improving overall administration of the procedure. Below, we provide an overview of these domains , with specific applications discussed in detail in the following section. 

As management begins with the recognition of an abnormality, CADe systems were the first area of interest for AI applications in endoscopy. Mirroring clinical practice, the data input for CADe systems is typically from standard endoscopy techniques, such as white light (WL) endoscopy. The application of CADe is best illustrated with the detection of colonic polyps, which is also the most established area of AI in endoscopy. Early CADe studies began with the recognition of polyp features in still frames to produce a binary outcome of whether or not a lesion was detected. With technological advances, CADe systems have evolved to assist the operator in the detection of potential colonic polyps in real time [ , top panel]. Several CADe systems for polyps are now commercially available around the world, such as GI Genius and ENDO-AID. Similar principles have been applied to other gastrointestinal pathologies including Barrett’s esophagus detection, obscure GI bleeding, and Crohn’s disease lesions within the small bowel. 

CADx systems provide a diagnosis by classifying lesions into known entities. As diagnosis often requires more characterization of features, these models may use contrast generating techniques (e.g., image-enhanced endoscopy—IEE) or high-magnification techniques (e.g., laser endomicroscopy and endocytoscopy). Thus, the output of CADx applications can be more complex and include grading, size, and characteristic elements normally only identifiable through ex vivo histopathological assessment. Similar to CADe, the most prominent example of CADx application remains the diagnosis of colorectal polyp histopathology, with several available commercial models (e.g., EndoBRAIN), and may be integrated with CADe models (e.g., CAD EYE). Combined CADe/CADx models provide a powerful workflow that can assist endoscopists in both the identification of colorectal neoplasia and the prediction of histopathology to inform management. Other CADx applications include gastric mucosa assessment and grading of inflammatory bowel diseases. 

Most recently, AI systems have been applied to quality assessment in endoscopy. These CADq systems have the most diverse reportable outcomes, which may include clinical metrics, such as adenoma detection rate (ADR), or systems outcomes such as healthcare costs . We propose a classification of CADq applications in endoscopy into “procedure assurance” and “procedure administration.” Procedure assurance applications focus on improving image quality and operator performance, such as bowel preparation assessment, monitoring for blind spots, and adequacy of mucosal exposure during endoscopy. Procedure administration applications standardize quality indicators and increase the efficiency of administering endoscopic procedures. These include automatic identification of landmarks with photo documentation and timing of procedures. 

Artificial intelligence applications in the esophagus Within the esophagus, the primary applications of AI have been in the detection of esophageal metaplasia and neoplasia. Barrett’s esophagus is a major risk factor for esophageal adenocarcinoma, but foci of neoplasia within Barrett’s esophagus may be missed by endoscopists at a rate of approximately 25% for high-grade dysplasia and esophageal adenocarcinoma within 1 year of a negative index examination. Using a large database of nearly 500,000 Barrett’s esophagus images, de Groof et al . developed a CADe system with an accuracy of 89% in detecting early neoplasia in patients with Barrett’s esophagus and outperformed all 53 endoscopists included in the study. A CADx system from Ebigbo et al . for real-time diagnosis of early adenocarcinoma in patients with Barrett’s esophagus showed an accuracy of 89.9%. More recent work from this group is shown in with the CADx platform showcasing diagnosis of dysplasia in Barrett’s esophagus where a segmented overlay of the endoscopy output is highlighted. In addition, a pilot study from this group also described the first multicenter application of an AI-based system in the prediction of submucosal invasion, allowing for the differentiation of T1a from T1b cancers in Barrett’s. Esophageal squamous cell carcinoma (SCC) makes up approximately two-thirds of missed esophageal cancers; several CADe studies have reported excellent sensitivities of greater than 98% at the detection of esophageal SCC. Diagnosis of SCC is critical as it changes management and may allow for less invasive endoscopic resection techniques. Several CADx models using WL/narrow band imaging (NBI) have demonstrated high accuracy and were on par with or outperformed endoscopists in differentiating invasion depth in mucosal and submucosal microinvasive (SM1) cancers from submucosal deeply invasive lesions (SM2/3). Notably, these CADe/CADx systems for esophageal SCC are largely tested with high-quality image sets with IEE, while real-time video detection continues to lag behind, as evidenced by a low per-frame sensitivity of 60.8% in recent studies. Artificial intelligence applications in the stomach The majority of efforts in AI development in the stomach have been in the detection and diagnosis of gastric neoplasms. Following initial studies at the detection of gastric malignancy, a large prospective multicenter case–control study by Luo et al . was done using over 1 million images from 84,424 patients, which were used to develop and test a CADe system. This system achieved a sensitivity of 94.2%, which was on par with expert endoscopists. CADx of gastric neoplasia studies has focused not only on differentiation from benign lesions with good results but also assist in the determination of invasion depth, which informs treatment options. Another common gastric AI application is the diagnosis of H. pylori infection, whereby AI systems may obviate the need for gastric biopsies in patients with low suspicion of H. pylori infection. Standard WL imaging was used to develop an AI model with a sensitivity of 88.9% and specificity of 87.4%, outperforming endoscopists who had an overall sensitivity of 79.0% and specificity of 83.2%. A more recent study using blue laser imaging and linked color imaging had even higher sensitivity of 96.7%. Small bowel assessment Wireless capsule endoscopy is a crucial tool in the assessment of the small bowel, but its interpretation by human operators is limited by extensive time and effort required, imprecision of the human eye, and attention span required to assess all frames. The most common indication for capsule endoscopy is obscure GI bleeding (OGIB), with a variable diagnostic yield of 40-80%. Detection of OGIB by the most recent CNN-based CADe systems has reported remarkable sensitivities, specificities, and accuracies of over 99%. Leenhardt et al . developed a system for detecting angioectasias with a sensitivity of 98.8%. Another common indication for capsule endoscopy is definite or suspected small bowel Crohn’s disease, for which capsule endoscopy has a poor diagnostic yield of approximately 50%. CADe systems designed to detect erosions and ulcerations of Crohn’s disease achieved sensitivities of 88-97%. Deep enteroscopy for the evaluation of the small bowel is a specialized and difficult technique. In a small 72-patient study, an AI CADe system has been developed to diagnose tumors via automatic detection of bowel wall protrusions, and achieved sensitivity and specificity of 97.0% and 97.4%, respectively. Large bowel applications of artificial intelligence Within the colon, perhaps the single largest application of AI with the largest number of randomized controlled trials has been the detection and diagnosis of polyps. In a systematic review of five trials, CADe systems achieved an overall ADR of 36.6% compared to 25.2% in a control group. This was true irrespective of adenoma size, location, or morphology including for sessile serrated polyps, which are historically difficult to detect. CADx using WL and NBI has also been assessed in multiple clinical trials and summarized in a systematic review of 18 studies, which identified an overall sensitivity of 92.3% for histologic prediction of polyps, and this was increased in reports using only NBI. When applied to real-time diagnosis of diminutive colorectal polyps, a recent CADx system (the POLyp Artificial Recognition system—POLAR) demonstrated similar diagnostic accuracy for neoplastic lesions compared to endoscopists and provided a diagnosis for 98% of polyps. High-magnification studies using endocytoscopy and confocal laser microendoscopy have also been used to develop CADx systems for colorectal polyps with excellent results. A recent open-label, prospective study using endocytoscopic images attained 96.9% sensitivity, 100% specificity, a 100% positive predictive value, and a 94.6% negative predictive value for colonic neoplasia, ultimately outperforming both endoscopy trainees and experts. Diagnosis is particularly important for diminutive polyps (<5mm), which may be resected and discarded if benign histopathology can be confidently predicted. To that extent, some AI studies have taken a special focus on the assessment of diminutive polyps. Another growing application for AI in the colon is the automatic assessment and diagnosis of inflammatory bowel disease . Inflammatory bowel disease grading and assessment has been an active area of research, including the use of optical biopsy techniques, such as confocal laser endomicroscopy, to identify crypt abscesses and other histologic-level evidence of inflammation. Several studies employing WL endoscopy have been used to generate objective measures of disease severity, including a prospective study, which was able to identify endoscopic remission with 90.1% accuracy. There are AI applications in development that account for every endoscopically accessible GI compartment . 

Within the esophagus, the primary applications of AI have been in the detection of esophageal metaplasia and neoplasia. Barrett’s esophagus is a major risk factor for esophageal adenocarcinoma, but foci of neoplasia within Barrett’s esophagus may be missed by endoscopists at a rate of approximately 25% for high-grade dysplasia and esophageal adenocarcinoma within 1 year of a negative index examination. Using a large database of nearly 500,000 Barrett’s esophagus images, de Groof et al . developed a CADe system with an accuracy of 89% in detecting early neoplasia in patients with Barrett’s esophagus and outperformed all 53 endoscopists included in the study. A CADx system from Ebigbo et al . for real-time diagnosis of early adenocarcinoma in patients with Barrett’s esophagus showed an accuracy of 89.9%. More recent work from this group is shown in with the CADx platform showcasing diagnosis of dysplasia in Barrett’s esophagus where a segmented overlay of the endoscopy output is highlighted. In addition, a pilot study from this group also described the first multicenter application of an AI-based system in the prediction of submucosal invasion, allowing for the differentiation of T1a from T1b cancers in Barrett’s. Esophageal squamous cell carcinoma (SCC) makes up approximately two-thirds of missed esophageal cancers; several CADe studies have reported excellent sensitivities of greater than 98% at the detection of esophageal SCC. Diagnosis of SCC is critical as it changes management and may allow for less invasive endoscopic resection techniques. Several CADx models using WL/narrow band imaging (NBI) have demonstrated high accuracy and were on par with or outperformed endoscopists in differentiating invasion depth in mucosal and submucosal microinvasive (SM1) cancers from submucosal deeply invasive lesions (SM2/3). Notably, these CADe/CADx systems for esophageal SCC are largely tested with high-quality image sets with IEE, while real-time video detection continues to lag behind, as evidenced by a low per-frame sensitivity of 60.8% in recent studies. 

The majority of efforts in AI development in the stomach have been in the detection and diagnosis of gastric neoplasms. Following initial studies at the detection of gastric malignancy, a large prospective multicenter case–control study by Luo et al . was done using over 1 million images from 84,424 patients, which were used to develop and test a CADe system. This system achieved a sensitivity of 94.2%, which was on par with expert endoscopists. CADx of gastric neoplasia studies has focused not only on differentiation from benign lesions with good results but also assist in the determination of invasion depth, which informs treatment options. Another common gastric AI application is the diagnosis of H. pylori infection, whereby AI systems may obviate the need for gastric biopsies in patients with low suspicion of H. pylori infection. Standard WL imaging was used to develop an AI model with a sensitivity of 88.9% and specificity of 87.4%, outperforming endoscopists who had an overall sensitivity of 79.0% and specificity of 83.2%. A more recent study using blue laser imaging and linked color imaging had even higher sensitivity of 96.7%. 

Wireless capsule endoscopy is a crucial tool in the assessment of the small bowel, but its interpretation by human operators is limited by extensive time and effort required, imprecision of the human eye, and attention span required to assess all frames. The most common indication for capsule endoscopy is obscure GI bleeding (OGIB), with a variable diagnostic yield of 40-80%. Detection of OGIB by the most recent CNN-based CADe systems has reported remarkable sensitivities, specificities, and accuracies of over 99%. Leenhardt et al . developed a system for detecting angioectasias with a sensitivity of 98.8%. Another common indication for capsule endoscopy is definite or suspected small bowel Crohn’s disease, for which capsule endoscopy has a poor diagnostic yield of approximately 50%. CADe systems designed to detect erosions and ulcerations of Crohn’s disease achieved sensitivities of 88-97%. Deep enteroscopy for the evaluation of the small bowel is a specialized and difficult technique. In a small 72-patient study, an AI CADe system has been developed to diagnose tumors via automatic detection of bowel wall protrusions, and achieved sensitivity and specificity of 97.0% and 97.4%, respectively. 

Within the colon, perhaps the single largest application of AI with the largest number of randomized controlled trials has been the detection and diagnosis of polyps. In a systematic review of five trials, CADe systems achieved an overall ADR of 36.6% compared to 25.2% in a control group. This was true irrespective of adenoma size, location, or morphology including for sessile serrated polyps, which are historically difficult to detect. CADx using WL and NBI has also been assessed in multiple clinical trials and summarized in a systematic review of 18 studies, which identified an overall sensitivity of 92.3% for histologic prediction of polyps, and this was increased in reports using only NBI. When applied to real-time diagnosis of diminutive colorectal polyps, a recent CADx system (the POLyp Artificial Recognition system—POLAR) demonstrated similar diagnostic accuracy for neoplastic lesions compared to endoscopists and provided a diagnosis for 98% of polyps. High-magnification studies using endocytoscopy and confocal laser microendoscopy have also been used to develop CADx systems for colorectal polyps with excellent results. A recent open-label, prospective study using endocytoscopic images attained 96.9% sensitivity, 100% specificity, a 100% positive predictive value, and a 94.6% negative predictive value for colonic neoplasia, ultimately outperforming both endoscopy trainees and experts. Diagnosis is particularly important for diminutive polyps (<5mm), which may be resected and discarded if benign histopathology can be confidently predicted. To that extent, some AI studies have taken a special focus on the assessment of diminutive polyps. Another growing application for AI in the colon is the automatic assessment and diagnosis of inflammatory bowel disease . Inflammatory bowel disease grading and assessment has been an active area of research, including the use of optical biopsy techniques, such as confocal laser endomicroscopy, to identify crypt abscesses and other histologic-level evidence of inflammation. Several studies employing WL endoscopy have been used to generate objective measures of disease severity, including a prospective study, which was able to identify endoscopic remission with 90.1% accuracy. There are AI applications in development that account for every endoscopically accessible GI compartment . 

Gastroenterology is the leading medical field for artificial intelligence innovations, and the number of prospective and clinical trials is only expected to grow. It is no surprise that AI has also been applied to advanced endoscopic therapeutic modalities and novel techniques, which are still being integrated into everyday practice. Cholangioscopy Cholangioscopy enables direct endoscopic visualization of the biliary and pancreatic ductal systems allowing detailed assessment of malignant strictures. In the diagnosis of malignant biliary strictures, the best-studied human operator cholangioscopy platform only achieved a moderate pooled sensitivity of 60.1% in a review of 10 studies. Saraiva et al . developed a CNN-based AI system, which achieved a sensitivity of 94.7% and a specificity of 92.1%. Although this was done in a small cohort of 85 patients, it demonstrates the utility of AI applications in novel technologies and suggests that novel endoscopic applications may benefit from parallel development with an AI counterpart. Third-space endoscopy Due to the nature of submucosal dissection, bleeding and perforation remain significant complications of third-space endoscopy. A deep learning algorithm for the real-time detection and delineation of relevant structures during third-space endoscopy was developed by Ebigbo et al . This was capable of detecting muscle layers and identifying vessels that cause intraprocedural bleeding. This demonstrates the ability of AI systems to support real-time clinical decisions intraoperatively in novel applications. Endoscopic ultrasound Endoscopic ultrasound (EUS) is an advanced imaging modality with superior performance over cross-sectional imaging for pancreatic and other lesions. However, this is dependent on operator clinical experience, thus limiting performance to the human learning curve. In a systematic review of AI systems analyzing EUS pancreas images, CNN-based models were capable of differentiation of benign versus malignant IPMN with a sensitivity of 95.7% and specificity of 92.6%, which are higher compared to historical data of human EUS performance. Another application of EUS is examination of gastric subepithelial lesions, for which no definitive EUS criteria exist to differentiate between malignant and benign lesions. Using CNN-based CADx systems that can account for pixel-level detail, endoscopists were able to achieve higher accuracies in diagnosing common lesions. Integrated systems A significant near-future development in AI systems is the integration of applications into a single platform. The initial studies and clinical trials covered in this review, and in general, focus on a single capability of AI, whether it is CADe, CADx, or other quantitative or qualitative features. However, it is important that real-world practical, efficient, and cost-effective AI systems can run in tandem with minimal human input or interruption during a procedure. As a demonstration of an integrated system, Su et al . developed an AI system in colonoscopy that monitored timing of withdrawal, withdrawal stability, and grading bowel preparation while also providing CADe of colonic polyps. The benefits of improving operator performance in an integrated system are highlighted by a 4-arm study, which compared CADe vs CADq systems and found higher ADR in the CADq arm. However, the two systems together worked synergistically, indicating that a combined system focused on improving operator performance and quality assurance—while assisting in diagnosis and detection—likely optimizes clinical outcomes. 

Cholangioscopy enables direct endoscopic visualization of the biliary and pancreatic ductal systems allowing detailed assessment of malignant strictures. In the diagnosis of malignant biliary strictures, the best-studied human operator cholangioscopy platform only achieved a moderate pooled sensitivity of 60.1% in a review of 10 studies. Saraiva et al . developed a CNN-based AI system, which achieved a sensitivity of 94.7% and a specificity of 92.1%. Although this was done in a small cohort of 85 patients, it demonstrates the utility of AI applications in novel technologies and suggests that novel endoscopic applications may benefit from parallel development with an AI counterpart. 

Due to the nature of submucosal dissection, bleeding and perforation remain significant complications of third-space endoscopy. A deep learning algorithm for the real-time detection and delineation of relevant structures during third-space endoscopy was developed by Ebigbo et al . This was capable of detecting muscle layers and identifying vessels that cause intraprocedural bleeding. This demonstrates the ability of AI systems to support real-time clinical decisions intraoperatively in novel applications. 

Endoscopic ultrasound (EUS) is an advanced imaging modality with superior performance over cross-sectional imaging for pancreatic and other lesions. However, this is dependent on operator clinical experience, thus limiting performance to the human learning curve. In a systematic review of AI systems analyzing EUS pancreas images, CNN-based models were capable of differentiation of benign versus malignant IPMN with a sensitivity of 95.7% and specificity of 92.6%, which are higher compared to historical data of human EUS performance. Another application of EUS is examination of gastric subepithelial lesions, for which no definitive EUS criteria exist to differentiate between malignant and benign lesions. Using CNN-based CADx systems that can account for pixel-level detail, endoscopists were able to achieve higher accuracies in diagnosing common lesions. 

A significant near-future development in AI systems is the integration of applications into a single platform. The initial studies and clinical trials covered in this review, and in general, focus on a single capability of AI, whether it is CADe, CADx, or other quantitative or qualitative features. However, it is important that real-world practical, efficient, and cost-effective AI systems can run in tandem with minimal human input or interruption during a procedure. As a demonstration of an integrated system, Su et al . developed an AI system in colonoscopy that monitored timing of withdrawal, withdrawal stability, and grading bowel preparation while also providing CADe of colonic polyps. The benefits of improving operator performance in an integrated system are highlighted by a 4-arm study, which compared CADe vs CADq systems and found higher ADR in the CADq arm. However, the two systems together worked synergistically, indicating that a combined system focused on improving operator performance and quality assurance—while assisting in diagnosis and detection—likely optimizes clinical outcomes. 

AI development in the field of gastrointestinal endoscopy continues to grow exponentially in a number of applications and supporting studies. With the advent of regulatory body-approved and marketed AI systems, these applications can be further refined and integrated into everyday practice. We provided an overview of this tremendous body of literature by organizing existing CADe, CADx, and CADq systems by their purpose and outcomes in studies. In reviewed studies, gastrointestinal endoscopists supported by AI systems have already achieved superior performance in previously challenging areas. Familiarity with AI applications can facilitate its growth, leading to endless opportunities to optimize clinical practice and outcomes. Financial support and sponsorship Nil. Conflicts of interest Michael Byrne is the Chief Executive Officer and founder of Satisfai Health. Other authors have nothing to disclose. 

Nil. 

Michael Byrne is the Chief Executive Officer and founder of Satisfai Health. Other authors have nothing to disclose. 

