Clinical evaluation of a real-time artificial intelligence-based polyp detection system: a US multi-center pilot study

Enhancing human-AI collaboration: The case of colonoscopy

Diagnostic errors impact patient health and healthcare costs. Artificial Intelligence (AI) shows promise in mitigating this burden by supporting Medical Doctors in decision-making. However, the mere display of excellent or even superhuman performance by AI in specific tasks does not guarantee a positive impact on medical practice. Effective AI assistance should target the primary causes of human errors and foster effective collaborative decision-making with human experts who remain the ultimate decision-makers. In this narrative review, we apply these principles to the specific scenario of AI assistance during colonoscopy. By unraveling the neurocognitive foundations of the colonoscopy procedure, we identify multiple bottlenecks in perception, attention, and decision-making that contribute to diagnostic errors, shedding light on potential interventions to mitigate them. Furthermore, we explored how existing AI devices fare in clinical practice and whether they achieved an optimal integration with the human decision-maker. We argue that to foster optimal Human-AI collaboration, future research should expand our knowledge of factors influencing AI's impact, establish evidence-based cognitive models, and develop training programs based on them. These efforts will enhance human-AI collaboration, ultimately improving diagnostic accuracy and patient outcomes. The principles illuminated in this review hold more general value, extending their relevance to a wide array of medical procedures and beyond.

Lack of Effectiveness of Computer Aided Detection for Colorectal Neoplasia: A Systematic Review and Meta-Analysis of Nonrandomized Studies

BACKGROUND AND AIMS

Benefits of computer-aided detection (CADe) in detecting colorectal neoplasia were shown in many randomized trials in which endoscopists' behavior was strictly controlled. However, the effect of CADe on endoscopists' performance in less-controlled setting is unclear. This systematic review and meta-analyses were aimed at clarifying benefits and harms of using CADe in real-world colonoscopy.

METHODS

We searched MEDLINE, EMBASE, Cochrane, and Google Scholar from inception to August 20, 2023. We included nonrandomized studies that compared the effectiveness between CADe-assisted and standard colonoscopy. Two investigators independently extracted study data and quality. Pairwise meta-analysis was performed utilizing risk ratio for dichotomous variables and mean difference (MD) for continuous variables with a 95% confidence interval (CI).

RESULTS

Eight studies were included, comprising 9782 patients (4569 with CADe and 5213 without CADe). Regarding benefits, there was a difference in neither adenoma detection rate (44% vs 38%; risk ratio, 1.11; 95% CI, 0.97 to 1.28) nor mean adenomas per colonoscopy (0.93 vs 0.79; MD, 0.14; 95% CI, -0.04 to 0.32) between CADe-assisted and standard colonoscopy, respectively. Regarding harms, there was no difference in the mean non-neoplastic lesions per colonoscopy (8 studies included for analysis; 0.52 vs 0.47; MD, 0.14; 95% CI, -0.07 to 0.34) and withdrawal time (6 studies included for analysis; 14.3 vs 13.4 minutes; MD, 0.8 minutes; 95% CI, -0.18 to 1.90). There was a substantial heterogeneity, and all outcomes were graded with a very low certainty of evidence.

CONCLUSION

CADe in colonoscopies neither improves the detection of colorectal neoplasia nor increases burden of colonoscopy in real-world, nonrandomized studies, questioning the generalizability of the results of randomized trials.

Impact of study design on adenoma detection in the evaluation of artificial intelligence-aided colonoscopy: a systematic review and meta-analysis

BACKGROUND AND AIMS

Randomized controlled trials (RCTs) have reported that artificial intelligence (AI) improves endoscopic polyp detection. Different methodologies-namely, parallel and tandem designs-have been used to evaluate the efficacy of AI-assisted colonoscopy in RCTs. Systematic reviews and meta-analyses have reported a pooled effect that includes both study designs. However, it is unclear whether there are inconsistencies in the reported results of these 2 designs. Here, we aimed to determine whether study characteristics moderate between-trial differences in outcomes when evaluating the effectiveness of AI-assisted polyp detection.

METHODS

A systematic search of Ovid MEDLINE, Embase, Cochrane Central, Web of Science, and IEEE Xplore was performed through March 1, 2023, for RCTs comparing AI-assisted colonoscopy with routine high-definition colonoscopy in polyp detection. The primary outcome of interest was the impact of study type on the adenoma detection rate (ADR). Secondary outcomes included the impact of the study type on adenomas per colonoscopy and withdrawal time, as well as the impact of geographic location, AI system, and endoscopist experience on ADR. Pooled event analysis was performed using a random-effects model.

RESULTS

Twenty-four RCTs involving 17,413 colonoscopies (AI assisted: 8680; non-AI assisted: 8733) were included. AI-assisted colonoscopy improved overall ADR (risk ratio [RR], 1.24; 95% confidence interval [CI], 1.17-1.31; I2 = 53%; P < .001). Tandem studies collectively demonstrated improved ADR in AI-aided colonoscopies (RR, 1.18; 95% CI, 1.08-1.30; I2 = 0%; P < .001), as did parallel studies (RR, 1.26; 95% CI, 1.17-1.35; I2 = 62%; P < .001), with no statistical subgroup difference between study design. Both tandem and parallel study designs revealed improvement in adenomas per colonoscopy in AI-aided colonoscopies, but this improvement was more marked among tandem studies (P < .001). AI assistance significantly increased withdrawal times for parallel (P = .002), but not tandem, studies. ADR improvement was more marked among studies conducted in Asia compared to Europe and North America in a subgroup analysis (P = .007). Type of AI system used or endoscopist experience did not affect overall improvement in ADR.

CONCLUSIONS

Either parallel or tandem study design can capture the improvement in ADR resulting from the use of AI-assisted polyp detection systems. Tandem studies powered to detect differences in endoscopic performance through paired comparison may be a resource-efficient method of evaluating new AI-assisted technologies.

Single Versus Second Observer vs Artificial Intelligence to Increase the ADENOMA Detection Rate of Colonoscopy-A Network Analysis

BACKGROUND AND AIMS

Screening colonoscopy has significantly contributed to the reduction of the incidence of colorectal cancer (CRC) and its associated mortality, with adenoma detection rate (ADR) as the quality marker. To increase the ADR, various solutions have been proposed including the utilization of Artificial Intelligence (AI) and employing second observers during colonoscopies. In the interest of AI improving ADR independently, without a second observer, and the operational similarity between AI and second observer, this network meta-analysis aims at evaluating the effectiveness of AI, second observer, and a single observer in improving ADR.

METHODS

We searched the Medline, Embase, Cochrane, Web of Science Core Collection, Korean Citation Index, SciELO, Global Index Medicus, and Cochrane. A direct head-to-head comparator analysis and network meta-analysis were performed using the random-effects model. The odds ratio (OR) was calculated with a 95% confidence interval (CI) and p-value < 0.05 was considered statistically significant.

RESULTS

We analyzed 26 studies, involving 22,560 subjects. In the direct comparative analysis, AI demonstrated higher ADR (OR: 0.668, 95% CI 0.595-0.749, p < 0.001) than single observer. Dual observer demonstrated a higher ADR (OR: 0.771, 95% CI 0.688-0.865, p < 0.001) than single operator. In network meta-analysis, results were consistent on the network meta-analysis, maintaining consistency. No statistical difference was noted when comparing AI to second observer. (RR 1.1 (0.9-1.2, p = 0.3). Results were consistent when evaluating only RCTs. Net ranking provided higher score to AI followed by second observer followed by single observer.

CONCLUSION

Artificial Intelligence and second-observer colonoscopy showed superior success in Adenoma Detection Rate when compared to single-observer colonoscopy. Although not statistically significant, net ranking model favors the superiority of AI to the second observer.

Artificial Intelligence-Assisted Colonoscopy in Real-World Clinical Practice: A Systematic Review and Meta-Analysis

INTRODUCTION

Artificial intelligence (AI) could minimize the operator-dependent variation in colonoscopy quality. Computer-aided detection (CADe) has improved adenoma detection rate (ADR) and adenomas per colonoscopy (APC) in randomized controlled trials. There is a need to assess the impact of CADe in real-world settings.

METHODS

We searched MEDLINE, EMBASE, and Web of Science for nonrandomized real-world studies of CADe in colonoscopy. Random-effects meta-analyses were performed to examine the effect of CADe on ADR and APC. The study is registered under PROSPERO (CRD42023424037). There was no funding for this study.

RESULTS

Twelve of 1,314 studies met inclusion criteria. Overall, ADR was statistically significantly higher with vs without CADe (36.3% vs 35.8%, risk ratio [RR] 1.13, 95% confidence interval [CI] 1.01-1.28). This difference remained significant in subgroup analyses evaluating 6 prospective (37.3% vs 35.2%, RR 1.15, 95% CI 1.01-1.32) but not 6 retrospective (35.7% vs 36.2%, RR 1.12, 95% CI 0.92-1.36) studies. Among 6 studies with APC data, APC rate ratio with vs without CADe was 1.12 (95% CI 0.95-1.33). In 4 studies with GI Genius (Medtronic), there was no difference in ADR with vs without CADe (RR 0.96, 95% CI 0.85-1.07).

DISCUSSION

ADR, but not APC, was slightly higher with vs without CADe among all available real-world studies. This difference was attributed to the results of prospective but not retrospective studies. The discrepancies between these findings and those of randomized controlled trials call for future research on the true impact of current AI technology on colonoscopy quality and the subtleties of human-AI interactions.

AI co-pilot bronchoscope robot

The unequal distribution of medical resources and scarcity of experienced practitioners confine access to bronchoscopy primarily to well-equipped hospitals in developed regions, contributing to the unavailability of bronchoscopic services in underdeveloped areas. Here, we present an artificial intelligence (AI) co-pilot bronchoscope robot that empowers novice doctors to conduct lung examinations as safely and adeptly as experienced colleagues. The system features a user-friendly, plug-and-play catheter, devised for robot-assisted steering, facilitating access to bronchi beyond the fifth generation in average adult patients. Drawing upon historical bronchoscopic videos and expert imitation, our AI-human shared control algorithm enables novice doctors to achieve safe steering in the lung, mitigating misoperations. Both in vitro and in vivo results underscore that our system equips novice doctors with the skills to perform lung examinations as expertly as seasoned practitioners. This study offers innovative strategies to address the pressing issue of medical resource disparities through AI assistance.

Evaluation of computer aided detection during colonoscopy among Veterans: Randomized clinical trial

BACKGROUND

There has been significant interest in use of computer aided detection (CADe) devices in colonoscopy to improve polyp detection and reduce miss rate.

AIM

To investigate the use of CADe amongst veterans.

METHODS

Between September 2020 and December 2021, we performed a randomized controlled trial to evaluate the impact of CADe. Patients at Veterans Affairs Palo Alto Health Care System presenting for screening or low-risk surveillance were randomized to colonoscopy performed with or without CADe. Primary outcomes of interest included adenoma detection rate (ADR), adenomas per colonoscopy (APC), and adenomas per extraction. In addition, we measured serrated polyps per colonoscopy, non-adenomatous, non-serrated polyps per colonoscopy, serrated polyp detection rate, and procedural time.

RESULTS

A total of 244 patients were enrolled (124 with CADe), with similar patient characteristics (age, sex, body mass index, indication) between the two groups. Use of CADe was found to have decreased number of adenomas (1.79 vs 2.53, P = 0.030) per colonoscopy compared to without CADe. There was no significant difference in number of serrated polyps or non-adenomatous non-serrated polyps per colonoscopy between the two groups. Overall, use of CADe was found to have lower ADR (68.5% vs 80.0%, P = 0.041) compared to without use of CADe. Serrated polyp detection rate was lower with CADe (3.2% vs 7.5%) compared to without CADe, but this was not statistically significant (P = 0.137). There was no significant difference in withdrawal and procedure times between the two groups or in detection of adenomas per extraction (71.4% vs 73.1%, P = 0.613). No adverse events were identified.

CONCLUSION

While several randomized controlled trials have demonstrated improved ADR and APC with use of CADe, in this RCT performed at a center with high ADR, use of CADe was found to have decreased APC and ADR. Further studies are needed to understand the true impact of CADe on performance quality among endoscopists as well as determine criteria for endoscopists to consider when choosing to adopt CADe in their practices.

The Impact of Artificial Intelligence in Improving Polyp and Adenoma Detection Rate During Colonoscopy: Systematic-Review and Meta-Analysis

INTRODUCTION

Colonoscopy may detect colorectal polyp and facilitate its removal in order to prevent colorectal cancer. However, substantial miss rate for colorectal adenomas detection still occurred during screening colonoscopy procedure. Nowadays, artificial intelligence (AI) have been employed in trials to improve polyp detection rate (PDR) and adenoma detection rate (ADR). Therefore, we would like to determine the impact of AI in increasing PDR and ADR.

METHODS

The present study adhered to the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-analyses 2020 (PRISMA 2020) statement. To identify relevant literature, comprehensive searches were conducted on major scientific databases, including Pubmed, EBSCO-host, and Proquest. The search was limited to articles published up to November 30, 2022. Inclusion criteria for the study encompassed full-text accessibility, articles written in the English language, and randomized controlled trials (RCTs) that reported both ADR and PDR values, comparing conventional diagnostic methods with AI-aided approaches. To synthesize the data, we computed the combined pooled odds ratio (OR) using a random-effects model. This model was chosen due to the expectation of considerable heterogeneity among the selected studies. To evaluate potential publication bias, the Begg's funnel diagram was employed.

RESULTS

A total of 13 studies were included in this study. Colonoscopy with AI had significantly higher PDR compared to without AI (pooled OR 1.46, 95% CI 1.13-1.89, p = 0.003) and higher ADR (pooled OR 1.58, 95% CI 1.37-1.82, p < 0.00001). PDR analysis showed moderate heterogeneity between included studies (p = 0.004; I2=63%). Furthermore, ADR analysis showed moderate heterogeneity (p < 0.007; I2 = 57%). Additionally, the funnels plot of ADR and PDR analysis showed an asymmetry plot and low publication bias.

CONCLUSION

AI may improve colonoscopy result quality through improving PDR and ADR.

The Role of Artificial Intelligence in Prospective Real-Time Histological Prediction of Colorectal Lesions during Colonoscopy: A Systematic Review and Meta-Analysis

Artificial intelligence (AI) presents a novel platform for improving disease diagnosis. However, the clinical utility of AI remains limited to discovery studies, with poor translation to clinical practice. Current data suggests that 26% of diminutive pre-malignant lesions and 3.5% of colorectal cancers are missed during colonoscopies. The primary aim of this study was to explore the role of artificial intelligence in real-time histological prediction of colorectal lesions during colonoscopy. A systematic search using MeSH headings relating to "AI", "machine learning", "computer-aided", "colonoscopy", and "colon/rectum/colorectal" identified 2290 studies. Thirteen studies reporting real-time analysis were included. A total of 2958 patients with 5908 colorectal lesions were included. A meta-analysis of six studies reporting sensitivities (95% CI) demonstrated that endoscopist diagnosis was superior to a computer-assisted detection platform, although no statistical significance was reached (p = 0.43). AI applications have shown encouraging results in differentiating neoplastic and non-neoplastic lesions using narrow-band imaging, white light imaging, and blue light imaging. Other modalities include autofluorescence imaging and elastic scattering microscopy. The current literature demonstrates that despite the promise of new endoscopic AI models, they remain inferior to expert endoscopist diagnosis. There is a need to focus developments on real-time histological predictions prior to clinical translation to demonstrate improved diagnostic capabilities and time efficiency.

Evaluation of Computer-Aided Detection During Colonoscopy in the Community (AI-SEE): A Multicenter Randomized Clinical Trial

INTRODUCTION

There has been increasing interest in artificial intelligence in gastroenterology. To reduce miss rates during colonoscopy, there has been significant exploration in computer-aided detection (CADe) devices. In this study, we evaluate the use of CADe in colonoscopy in community-based, nonacademic practices.

METHODS

Between September 28, 2020, and September 24, 2021, a randomized controlled trial (AI-SEE) was performed evaluating the impact of CADe on polyp detection in 4 community-based endoscopy centers in the United States Patients were block-randomized to undergoing colonoscopy with or without CADe (EndoVigilant). Primary outcomes measured were adenomas per colonoscopy and adenomas per extraction (the percentage of polyps removed that are adenomas). Secondary end points included serrated polyps per colonoscopy; nonadenomatous, nonserrated polyps per colonoscopy; adenoma and serrated polyp detection rates; and procedural time.

RESULTS

A total of 769 patients were enrolled (387 with CADe), with similar patient demographics between the 2 groups. There was no significant difference in adenomas per colonoscopy in the CADe and non-CADe groups (0.73 vs 0.67, P = 0.496). Although the use of CADe did not improve identification of serrated polyps per colonoscopy (0.08 vs 0.08, P = 0.965), the use of CADe increased identification of nonadenomatous, nonserrated polyps per colonoscopy (0.90 vs 0.51, P < 0.0001), resulting in detection of fewer adenomas per extraction in the CADe group. The adenoma detection rate (35.9 vs 37.2%, P = 0.774) and serrated polyp detection rate (6.5 vs 6.3%, P = 1.000) were similar in the CADe and non-CADe groups. Mean withdrawal time was longer in the CADe group compared with the non-CADe group (11.7 vs 10.7 minutes, P = 0.003). However, when no polyps were identified, there was similar mean withdrawal time (9.1 vs 8.8 minutes, P = 0.288). There were no adverse events.

DISCUSSION

The use of CADe did not result in a statistically significant difference in the number of adenomas detected. Additional studies are needed to better understand why some endoscopists derive substantial benefits from CADe and others do not. ClinicalTrials.gov number: NCT04555135.

Colorectal cancer incidence trends by tumour location among adults of screening-age in England: a population-based study

AIM

Proximal and distal colorectal cancers (CRCs) exhibit different clinical, molecular and biological patterns. The aim of this study was to determine temporal trends in the age-standardized incidence rates (ASIRs) of proximal and distal CRC following the introduction of the English Bowel Cancer Screening Programme (BCSP) in 2006.

METHOD

The National Cancer Registration and Analysis Service database was used to identify incident cases of CRC among adults of screening age (60-74 years) between 2001 and 2017. ASIRs were calculated using the European Standard Population 2013 and incidence trends analysed by anatomical subsite (proximal, caecum to descending colon; distal, sigmoid to rectum), sex and Index of Multiple Deprivation (IMD) quintile using Joinpoint regression software.

RESULTS

Between 2001 and 2017, 541 515 incident cases of CRC were diagnosed [236 167 proximal (43.6%) and 305 348 distal (56.4%)]. A marginal reduction in the proximal ASIR was noted from 2008 [annual percentage change (APC) -1.4% (95% CI -2.0% to -0.9%)] compared with a greater reduction in distal ASIR from 2011 to 2014 [APC -6.6% (95% CI -11.5% to -1.5%)] which plateaued thereafter [APC -0.5% (95% CI -3.2% to 2.2%)]. Incidence rates decreased more rapidly in men than women. Adults in IMD quintiles 4-5 experienced the greatest reduction in distal tumours [APC -3.5% (95% CI -4.3% to -2.7%)].

CONCLUSION

Following the introduction of the English BCSP, the incidence of CRC has subsequently reduced among adults of screening age, with this trend being most pronounced in distal tumours and in men. There is also evidence of a reduction in the deprivation gap for distal tumour incidence. Strategies to improve the detection of proximal tumours are warranted.

Clinical Validation Benchmark Dataset and Expert Performance Baseline for Colorectal Polyp Localization Methods

Colorectal cancer is one of the leading death causes worldwide, but, fortunately, early detection highly increases survival rates, with the adenoma detection rate being one surrogate marker for colonoscopy quality. Artificial intelligence and deep learning methods have been applied with great success to improve polyp detection and localization and, therefore, the adenoma detection rate. In this regard, a comparison with clinical experts is required to prove the added value of the systems. Nevertheless, there is no standardized comparison in a laboratory setting before their clinical validation. The ClinExpPICCOLO comprises 65 unedited endoscopic images that represent the clinical setting. They include white light imaging and narrow band imaging, with one third of the images containing a lesion but, differently to another public datasets, the lesion does not appear well-centered in the image. Together with the dataset, an expert clinical performance baseline has been established with the performance of 146 gastroenterologists, who were required to locate the lesions in the selected images. Results shows statistically significant differences between experience groups. Expert gastroenterologists' accuracy was 77.74, while sensitivity and specificity were 86.47 and 74.33, respectively. These values can be established as minimum values for a DL method before performing a clinical trial in the hospital setting.

Automation in Contemporary Clinical Information Systems: a Survey of AI in Healthcare Settings

AIMS AND OBJECTIVES

To examine the nature and use of automation in contemporary clinical information systems by reviewing studies reporting the implementation and evaluation of artificial intelligence (AI) technologies in healthcare settings.

METHOD

PubMed/MEDLINE, Web of Science, EMBASE, the tables of contents of major informatics journals, and the bibliographies of articles were searched for studies reporting evaluation of AI in clinical settings from January 2021 to December 2022. We documented the clinical application areas and tasks supported, and the level of system autonomy. Reported effects on user experience, decision-making, care delivery and outcomes were summarised.

RESULTS

AI technologies are being applied in a wide variety of clinical areas. Most contemporary systems utilise deep learning, use routinely collected data, support diagnosis and triage, are assistive (requiring users to confirm or approve AI provided information or decisions), and are used by doctors in acute care settings in high-income nations. AI systems are integrated and used within existing clinical information systems including electronic medical records. There is limited support for One Health goals. Evaluation is largely based on quantitative methods measuring effects on decision-making.

CONCLUSION

AI systems are being implemented and evaluated in many clinical areas. There remain many opportunities to understand patterns of routine use and evaluate effects on decision-making, care delivery and patient outcomes using mixed-methods. Support for One Health including integrating data about environmental factors and social determinants needs further exploration.

Recent advances in devices and technologies that might prove revolutionary for colonoscopy procedures

INTRODUCTION

Colorectal cancer (CRC) is the third most common malignancy and second leading cause of cancer-related mortality in the world. Adenoma detection rate (ADR), a quality indicator for colonoscopy, has gained prominence as it is inversely related to CRC incidence and mortality. As such, recent efforts have focused on developing novel colonoscopy devices and technologies to improve ADR.

AREAS COVERED

The main objective of this paper is to provide an overview of advancements in the fields of colonoscopy mechanical attachments, artificial intelligence-assisted colonoscopy, and colonoscopy optical enhancements with respect to ADR. We accomplished this by performing a comprehensive search of multiple electronic databases from inception to September 2023. This review is intended to be an introduction to colonoscopy devices and technologies.

EXPERT OPINION

Numerous mechanical attachments and optical enhancements have been developed that have the potential to improve ADR and AI has gone from being an inaccessible concept to a feasible means for improving ADR. While these advances are exciting and portend a change in what will be considered standard colonoscopy, they continue to require refinement. Future studies should focus on combining modalities to further improve ADR and exploring the use of these technologies in other facets of colonoscopy.

Performance and attitudes toward real-time computer-aided polyp detection during colonoscopy in a large tertiary referral center in the United States

BACKGROUND AND AIMS

Computer-aided detection (CADe) has been shown to improve polyp detection in clinical trials. Limited data exist on the impact, utilization, and attitudes toward artificial intelligence (AI)-assisted colonoscopy in daily clinical practice. We aimed to evaluate the effectiveness of the first U.S. Food and Drug Administration-approved CADe device for polyp detection in the United States and the attitudes toward its implementation.

METHODS

We performed a retrospective analysis of a prospectively maintained database of patients undergoing colonoscopy at a tertiary center in the United States before and after a real-time CADe system was made available. The decision to activate the CADe system was at the discretion of the endoscopist. An anonymous survey was circulated among endoscopy physicians and staff at the beginning and conclusion of the study period regarding their attitudes toward AI-assisted colonoscopy.

RESULTS

CADe was activated in 52.1% of cases. Compared with historical control subjects, there was no statistically significant difference in adenomas detected per colonoscopy (1.08 vs 1.04, P = .65), even after excluding diagnostic and therapeutic indications and cases where CADe was not activated (1.27 vs 1.17, P = .45). In addition, there was no statistically significant difference in adenoma detection rate (ADR), median procedure, and withdrawal times. Survey results demonstrated mixed attitudes toward AI-assisted colonoscopy, of which main concerns were high number of false-positive signals (82.4%), high level of distraction (58.8%), and impression it prolonged procedure time (47.1%).

CONCLUSIONS

CADe did not improve adenoma detection in daily practice among endoscopists with high baseline ADRs. Despite its availability, AI-assisted colonoscopy was only activated in half of the cases, and multiple concerns were raised by staff and endoscopists. Future studies will help elucidate the patients and endoscopists that would benefit most from AI-assisted colonoscopy.

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.