1:  ESGE recommends cold snare polypectomy (CSP), to include a clear margin of normal tissue (1-2 mm) surrounding the polyp, for the removal of diminutive polyps (≤ 5 mm).Strong recommendation, high quality of evidence. 2:  ESGE recommends against the use of cold biopsy forceps excision because of its high rate of incomplete resection.Strong recommendation, moderate quality of evidence. 3:  ESGE recommends CSP, to include a clear margin of normal tissue (1-2 mm) surrounding the polyp, for the removal of small polyps (6-9 mm).Strong recommendation, high quality of evidence. 4:  ESGE recommends hot snare polypectomy for the removal of nonpedunculated adenomatous polyps of 10-19 mm in size.Strong recommendation, high quality of evidence. 5:  ESGE recommends conventional (diathermy-based) endoscopic mucosal resection (EMR) for large (≥ 20 mm) nonpedunculated adenomatous polyps (LNPCPs).Strong recommendation, high quality of evidence. 6:  ESGE suggests that underwater EMR can be considered an alternative to conventional hot EMR for the treatment of adenomatous LNPCPs.Weak recommendation, moderate quality of evidence. 7:  Endoscopic submucosal dissection (ESD) may also be suggested as an alternative for removal of LNPCPs of ≥ 20 mm in selected cases and in high-volume centers.Weak recommendation, low quality evidence. 8:  ESGE recommends that, after piecemeal EMR of LNPCPs by hot snare, the resection margins should be treated by thermal ablation using snare-tip soft coagulation to prevent adenoma recurrence.Strong recommendation, high quality of evidence. 9:  ESGE recommends (piecemeal) cold snare polypectomy or cold EMR for SSLs of all sizes without suspected dysplasia.Strong recommendation, moderate quality of evidence. 10:  ESGE recommends prophylactic endoscopic clip closure of the mucosal defect after EMR of LNPCPs in the right colon to reduce to reduce the risk of delayed bleeding.Strong recommendation, high quality of evidence. 11:  ESGE recommends that en bloc resection techniques, such as en bloc EMR, ESD, endoscopic intermuscular dissection, endoscopic full-thickness resection, or surgery should be the techniques of choice in cases with suspected superficial invasive carcinoma, which otherwise cannot be removed en bloc by standard polypectomy or EMR.Strong recommendation, moderate quality of evidence.European Society of Gastrointestinal Endoscopy. All rights reserved.

1:  ESGE suggests that high definition endoscopy, and dye or virtual chromoendoscopy, as well as add-on devices, can be used in average risk patients to increase the endoscopist's adenoma detection rate. However, their routine use must be balanced against costs and practical considerations.Weak recommendation, high quality evidence. 2:  ESGE recommends the routine use of high definition systems in individuals with Lynch syndrome.Strong recommendation, high quality evidence. 3:  ESGE recommends the routine use, with targeted biopsies, of dye-based pancolonic chromoendoscopy or virtual chromoendoscopy for neoplasia surveillance in patients with long-standing colitis.Strong recommendation, moderate quality evidence. 4:  ESGE suggests that virtual chromoendoscopy and dye-based chromoendoscopy can be used, under strictly controlled conditions, for real-time optical diagnosis of diminutive (≤ 5 mm) colorectal polyps and can replace histopathological diagnosis. The optical diagnosis has to be reported using validated scales, must be adequately photodocumented, and can be performed only by experienced endoscopists who are adequately trained, as defined in the ESGE curriculum, and audited.Weak recommendation, high quality evidence. 5:  ESGE recommends the use of high definition white-light endoscopy in combination with (virtual) chromoendoscopy to predict the presence and depth of any submucosal invasion in nonpedunculated colorectal polyps prior to any treatment. Strong recommendation, moderate quality evidence. 6:  ESGE recommends the use of virtual or dye-based chromoendoscopy in addition to white-light endoscopy for the detection of residual neoplasia at a piecemeal polypectomy scar site. Strong recommendation, moderate quality evidence. 7:  ESGE suggests the possible incorporation of computer-aided diagnosis (detection and characterization of lesions) to colonoscopy, if acceptable and reproducible accuracy for colorectal neoplasia is demonstrated in high quality multicenter in vivo clinical studies. Possible significant risks with implementation, specifically endoscopist deskilling and over-reliance on artificial intelligence, unrepresentative training datasets, and hacking, need to be considered. Weak recommendation, low quality evidence.© Georg Thieme Verlag KG Stuttgart · New York.

To drive translational medicine, modern day biobanks need to integrate with other sources of data (clinical, genomics) to support novel data-intensive research. Currently, vast amounts of research and clinical data remain in silos, held and managed by individual researchers, operating under different standards and governance structures; a framework that impedes sharing and effective use of data. In this article, we describe the journey of British Columbia's Gynecological Cancer Research Program (OVCARE) in moving a traditional tumour biobank, outcomes unit, and a collection of data silos, into an integrated data commons to support data standardization and resource sharing under collaborative governance, as a means of providing the gynecologic cancer research community in British Columbia access to tissue samples and associated clinical and molecular data from thousands of patients.Through several engagements with stakeholders from various research institutions within our research community, we identified priorities and assessed infrastructure needs required to optimize and support data collections, storage and sharing, under three main research domains: (1) biospecimen collections, (2) molecular and genomics data, and (3) clinical data. We further built a governance model and a resource portal to implement protocols and standard operating procedures for seamless collections, management and governance of interoperable data, making genomic, and clinical data available to the broader research community.Proper infrastructures for data collection, sharing and governance is a translational research imperative. We have consolidated our data holdings into a data commons, along with standardized operating procedures to meet research and ethics requirements of the gynecologic cancer community in British Columbia. The developed infrastructure brings together, diverse data, computing frameworks, as well as tools and applications for managing, analyzing, and sharing data. Our data commons bridges data access gaps and barriers to precision medicine and approaches for diagnostics, treatment and prevention of gynecological cancers, by providing access to large datasets required for data-intensive science.© 2021. The Author(s).

Introduction: Colonoscopy has a crucial role in reducing colorectal cancer incidence and mortality. Different artificial intelligence (AI) systems were developed to further improve its quality assurance (computer-aided quality improvement [CAQ]), lesion detection (computer-aided detection [CADe]), and lesion characterization (computer-aided characterization [CADx]). There were studies investigating the roles of these AI systems in different domains of standard colonoscopies. Methods: In this state-of-the-art narrative review, we summarize the current evidence, discuss existing limitations, as well as explore the future directions of AI in colonoscopy. Results: CAQ enhances colonoscopy quality through real-time feedback and quality monitoring systems, but the studies have inconsistent results due to small training datasets and varied methodologies. CADe increases adenoma detection rate and reduces adenoma missed rates, but there are concerns about false positives, unnecessary polypectomies, potential deskilling of endoscopists, and cost-effectiveness. CADx systems have mixed results and accuracies in differentiating polyp types, and its use is further hindered by inadequate representation of sessile serrated lesions and a lack of rigorous trials comparing it with standard colonoscopy. Conclusion: Despite the emerging evidence of AI-assisted colonoscopy, its potential drawbacks and limitations may hinder the further implementation in real-world clinical practice. Long-term data on clinical efficacy, cost-effectiveness, liability, and data sharing are the key areas to be addressed.

Research and development of artificial intelligence (AI) in the field of gastrointestinal endoscopy is progressing rapidly. In Japan alone, there are more than 10 AI‐assisted endoscopic medical devices that have received regulatory approval, and numerous randomized controlled trials have been published both domestically and internationally. However, the adoption of AI in clinical practice has not been smooth due to factors such as insufficient evaluation of the balance between clinical benefits and harms, unclear cost‐effectiveness, the lack of reliable guidelines, and the absence of established reimbursement systems for medical fees. Considering this situation, the Japan Gastroenterological Endoscopy Society (JGES) presents its perspective on the status of AI in endoscopic practice in the form of the following position statements. This comprises nine statements developed by the JGES AI Committee in collaboration with a diverse panel of members. These statements comprehensively address issues related to the quality of endoscopic examinations, cost‐effectiveness, clinical disadvantages, preparatory knowledge, medical safety, and legal responsibilities. They have been developed to be practical and useful in actual endoscopy settings.

Esophagogastroduodenoscopy (EGD) is the pivotal procedure in the diagnosis of upper gastrointestinal lesions. However, there are significant variations in EGD performance among endoscopists, impairing the discovery rate of gastric cancers and precursor lesions. The aim of this study was to construct a real-time quality improving system, WISENSE, to monitor blind spots, time the procedure and automatically generate photodocumentation during EGD and thus raise the quality of everyday endoscopy.WISENSE system was developed using the methods of deep convolutional neural networks and deep reinforcement learning. Patients referred because of health examination, symptoms, surveillance were recruited from Renmin hospital of Wuhan University. Enrolled patients were randomly assigned to groups that underwent EGD with or without the assistance of WISENSE. The primary end point was to ascertain if there was a difference in the rate of blind spots between WISENSE-assisted group and the control group.WISENSE monitored blind spots with an accuracy of 90.40% in real EGD videos. A total of 324 patients were recruited and randomised. 153 and 150 patients were analysed in the WISENSE and control group, respectively. Blind spot rate was lower in WISENSE group compared with the control (5.86% vs 22.46%, p<0.001), and the mean difference was -15.39% (95% CI -19.23 to -11.54). There was no significant adverse event.WISENSE significantly reduced blind spot rate of EGD procedure and could be used to improve the quality of everyday endoscopy.ChiCTR1800014809; Results.© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Colonoscopy is the primary tool for colorectal cancer screening. High-quality colonoscopy is crucial for the detection of precancerous adenomas; however, the adenoma detection rate varies depending on the skill and experience of the endoscopist. Computer-aided quality assessment (CAQ) uses artificial intelligence (AI) technology to evaluate the quality of colonoscopy examinations. It plays an important role in reducing variations in examination quality and obtaining high-quality colonoscopic images. In this review, we focus specifically on the speedometer, effective withdrawal time, fold examination quality, bowel preparation quality assessment, and cecal intubation with CAQ systems and discuss the role and effectiveness of these systems. CAQ systems are expected to contribute to increase in adenoma detection rates, improvement in endoscopist skills, and standardization of examination quality. However, challenges such as variability in AI performance across different clinical settings and potential overreliance on automated prompts remain key limitations.

We developed an artificial intelligence (AI)-based endoscopic ultrasonography (EUS) system for diagnosing the invasion depth of early gastric cancer (EGC), and we evaluated the performance of this system.A total of 8280 EUS images from 559 EGC cases were collected from 11 institutions. Within this dataset, 3451 images (285 cases) from one institution were used as a development dataset. The AI model consisted of segmentation and classification steps, followed by the CycleGAN method to bridge differences in EUS images captured by different equipment. AI model performance was evaluated using an internal validation dataset collected from the same institution as the development dataset (1726 images, 135 cases). External validation was conducted using images collected from the other 10 institutions (3103 images, 139 cases).The area under the curve (AUC) of the AI model in the internal validation dataset was 0.870 (95% CI: 0.796-0.944). Regarding diagnostic performance, the accuracy/sensitivity/specificity values of the AI model, experts (n = 6), and nonexperts (n = 8) were 82.2/63.4/90.4%, 81.9/66.3/88.7%, and 68.3/60.9/71.5%, respectively. The AUC of the AI model in the external validation dataset was 0.815 (95% CI: 0.743-0.886). The accuracy/sensitivity/specificity values of the AI model (74.1/73.1/75.0%) and the real-time diagnoses of experts (75.5/79.1/72.2%) in the external validation dataset were comparable.Our AI model demonstrated a diagnostic performance equivalent to that of experts.© 2024. The Author(s).

Multiple artificial intelligence (AI) systems have been developed to assist with endoscopic diagnosis. We established the first real-time AI lesion-labeling system to assist in delineating lesion margin during endoscopic submucosal dissection (ESD). We aimed to further validate the efficacy of this system in improving histological complete resection rate especially for beginners in low-volume centers.

While artificial intelligence (AI) shows high potential in decision support for diagnostic gastrointestinal endoscopy, its role in therapeutic endoscopy remains unclear. Third space endoscopic procedures pose the risk of intraprocedural bleeding. Therefore, we aimed to develop an AI algorithm for intraprocedural blood vessel detection.Using a test dataset with 101 standardized video clips containing 200 predefined submucosal blood vessels, 19 endoscopists were evaluated for the vessel detection rate (VDR) and time (VDT) with and without support of an AI algorithm. Test subjects were grouped according to experience in ESD.With AI support, endoscopists VDR increased from 56.4% [CI 54.1-58.6] to 72.4% [CI 70.3-74.4]. Endoscopists' VDT dropped from 6.7sec [CI 6.2-7.1] to 5.2sec [CI 4.8-5.7]. False positive (FP) readings appeared in 4.5% of frames and were marked significantly shorter than true positives (6.0sec [CI 5.28-6.70] vs. 0.7sec [CI 0.55-0.87]).AI improved the vessel detection rate and time of endoscopists during third space endoscopy. While these data need to be corroborated by clinical trials, AI may prove to be an invaluable tool for the improvement of endoscopic interventions.Thieme. All rights reserved.

\n Background The development of the EndoMaster “Endoluminal Access Surgical Efficacy” (EASE) system aims to enhance the safety and efficacy of colonic endoscopic submucosal dissection (ESD) through two flexible robotic arms. This is the first clinical trial to evaluate the performance of colorectal ESD using EndoMaster.

\nClear visualization during transnasal endoscopic surgery (TNES) is crucial for safe, efficient surgery.

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

As health care continues to evolve with technological advancements, the integration of artificial intelligence into clinical practices has shown promising potential to enhance patient care and operational efficiency. Among the forefront of these innovations are large language models (LLMs), a subset of artificial intelligence designed to understand, generate, and interact with human language at an unprecedented scale.

Large language models like ChatGPT, with their growing accessibility, are attracting increasing interest within the artificial intelligence medical field, particularly in the analysis of radiology reports. These present a valuable opportunity to explore the potential clinical applications of large language models, given their huge capabilities in processing and understanding written language. Early research indicates that ChatGPT could offer benefits in radiology reporting. ChatGPT can assist but not replace radiologists in achieving diagnoses, generating structured reports, extracting data, identifying errors or incidental findings, and can also serve as a support in creating patient-friendly reports. However, ChatGPT also has intrinsic limitations, such as hallucinations, stochasticity, biases, deficiencies in complex clinical scenarios, data privacy and legal concerns. To fully utilize the potential of ChatGPT in radiology reporting, careful integration planning and rigorous validation of their outputs are crucial, especially for tasks requiring abstract reasoning or nuanced medical context. Radiologists' expertise in medical imaging and data analysis positions them exceptionally well to lead the responsible integration and utilization of ChatGPT within the field of radiology. This article offers a topical overview of the potential strengths and limitations of ChatGPT in radiological reporting.© 2024. Italian Society of Medical Radiology.

Artificial intelligence (AI) research in healthcare is accelerating rapidly, with potential applications being demonstrated across various domains of medicine. However, there are currently limited examples of such techniques being successfully deployed into clinical practice. This article explores the main challenges and limitations of AI in healthcare, and considers the steps required to translate these potentially transformative technologies from research to clinical practice.Key challenges for the translation of AI systems in healthcare include those intrinsic to the science of machine learning, logistical difficulties in implementation, and consideration of the barriers to adoption as well as of the necessary sociocultural or pathway changes. Robust peer-reviewed clinical evaluation as part of randomised controlled trials should be viewed as the gold standard for evidence generation, but conducting these in practice may not always be appropriate or feasible. Performance metrics should aim to capture real clinical applicability and be understandable to intended users. Regulation that balances the pace of innovation with the potential for harm, alongside thoughtful post-market surveillance, is required to ensure that patients are not exposed to dangerous interventions nor deprived of access to beneficial innovations. Mechanisms to enable direct comparisons of AI systems must be developed, including the use of independent, local and representative test sets. Developers of AI algorithms must be vigilant to potential dangers, including dataset shift, accidental fitting of confounders, unintended discriminatory bias, the challenges of generalisation to new populations, and the unintended negative consequences of new algorithms on health outcomes.The safe and timely translation of AI research into clinically validated and appropriately regulated systems that can benefit everyone is challenging. Robust clinical evaluation, using metrics that are intuitive to clinicians and ideally go beyond measures of technical accuracy to include quality of care and patient outcomes, is essential. Further work is required (1) to identify themes of algorithmic bias and unfairness while developing mitigations to address these, (2) to reduce brittleness and improve generalisability, and (3) to develop methods for improved interpretability of machine learning predictions. If these goals can be achieved, the benefits for patients are likely to be transformational.

The number and variety of applications of artificial intelligence (AI) in gastrointestinal (GI) endoscopy is growing rapidly. New technologies based on machine learning (ML) and convolutional neural networks (CNNs) are at various stages of development and deployment to assist patients and endoscopists in preparing for endoscopic procedures, in detection, diagnosis and classification of pathology during endoscopy and in confirmation of key performance indicators. Platforms based on ML and CNNs require regulatory approval as medical devices. Interactions between humans and the technologies we use are complex and are influenced by design, behavioural and psychological elements. Due to the substantial differences between AI and prior technologies, important differences may be expected in how we interact with advice from AI technologies. Human-AI interaction (HAII) may be optimised by developing AI algorithms to minimise false positives and designing platform interfaces to maximise usability. Human factors influencing HAII may include automation bias, alarm fatigue, algorithm aversion, learning effect and deskilling. Each of these areas merits further study in the specific setting of AI applications in GI endoscopy and professional societies should engage to ensure that sufficient emphasis is placed on human-centred design in development of new AI technologies.©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.