Superficial esophageal squamous cell carcinoma (ESCC) detection is crucial. Although narrow-band imaging improves detection, its effectiveness is diminished by inexperienced endoscopists. The effects of artificial intelligence (AI) assistance on ESCC detection by endoscopists remain unclear. Therefore, this study aimed to develop and validate an AI model for ESCC detection using endoscopic video analysis and evaluate diagnostic improvements.

Endoscopic videos with and without ESCC lesions were collected from May 2020 to January 2022. The AI model trained on annotated videos and 18 endoscopists (eight experts, 10 non-experts) evaluated their diagnostic performance. After 4 weeks, the endoscopists re-evaluated the test data with AI assistance. Sensitivity, specificity, and accuracy were compared between endoscopists with and without AI assistance.

Training data comprised 280 cases (140 with and 140 without lesions), and test data, 115 cases (52 with and 63 without lesions). In the test data, the median lesion size was 14.5 mm (range: 1-100 mm), with pathological depths ranging from high-grade intraepithelial to submucosal neoplasia. The model's sensitivity, specificity, and accuracy were 76.0%, 79.4%, and 77.2%, respectively. With AI assistance, endoscopist sensitivity (57.4% vs. 66.5%) and accuracy (68.6% vs. 75.9%) improved significantly, while specificity increased slightly (87.0% vs. 91.6%). Experts demonstrated substantial improvements in sensitivity (59.1% vs. 70.0%) and accuracy (72.1% vs. 79.3%). Non-expert accuracy increased significantly (65.8% vs. 73.3%), with slight improvements in sensitivity (56.1% vs. 63.7%) and specificity (81.9% vs. 89.2%).

AI assistance enhances ESCC detection and improves endoscopists' diagnostic performance, regardless of experience.

Gastric cancer (GC) occupies the first few places in the world among tumors in terms of incidence and mortality, causing serious harm to human health, and at the same time, its treatment greatly consumes the health care resources of all countries in the world. The diagnosis of GC is usually based on histopathologic examination, and it is very important to be able to detect and identify cancerous lesions at an early stage, but some endoscopists' lack of diagnostic experience and fatigue at work lead to a certain rate of under diagnosis. The rapid and striking development of Artificial intelligence (AI) has helped to enhance the ability to extract abnormal information from endoscopic images to some extent, and more and more researchers are applying AI technology to the diagnosis of GC. This initiative has not only improved the detection rate of early gastric cancer (EGC), but also significantly improved the survival rate of patients after treatment. This article reviews the results of various AI-assisted diagnoses of EGC in recent years, including the identification of EGC, the determination of differentiation type and invasion depth, and the identification of borders. Although AI has a better application prospect in the early diagnosis of ECG, there are still major challenges, and the prospects and limitations of AI application need to be further discussed.

Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in cancer research, offering the ability to process huge data rapidly and make precise therapeutic decisions. Over the last decade, AI, particularly deep learning (DL) and machine learning (ML), has significantly enhanced cancer prediction, diagnosis, and treatment by leveraging algorithms such as convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs). These technologies provide reliable, efficient solutions for managing aggressive diseases like cancer, which have high recurrence and mortality rates. This review prospective highlights the applications of AI in oncology, a long with FDA-approved technologies like EFAI RTSuite CT HN-Segmentation System, Quantib Prostate, and Paige Prostate, and explore their role in advancing cancer detection, personalized care, and treatment. Furthermore, we also explored broader applications of AI in healthcare, addressing challenges, limitations, regulatory considerations, and ethical implications. By presenting these advancements, we underscore AI's potential to revolutionize cancer care, management and treatment.

At a population level, the European Society of Gastrointestinal Endoscopy (ESGE), the European Helicobacter and Microbiota Study Group (EHMSG), and the European Society of Pathology (ESP) suggest endoscopic screening for gastric cancer (and precancerous conditions) in high-risk regions (age-standardized rate [ASR] > 20 per 100 000 person-years) every 2 to 3 years or, if cost-effectiveness has been proven, in intermediate risk regions (ASR 10-20 per 100 000 person-years) every 5 years, but not in low-risk regions (ASR < 10).ESGE/EHMSG/ESP recommend that irrespective of country of origin, individual gastric risk assessment and stratification of precancerous conditions is recommended for first-time gastroscopy. ESGE/EHMSG/ESP suggest that gastric cancer screening or surveillance in asymptomatic individuals over 80 should be discontinued or not started, and that patients' comorbidities should be considered when treatment of superficial lesions is planned.ESGE/EHMSG/ESP recommend that a high quality endoscopy including the use of virtual chromoendoscopy (VCE), after proper training, is performed for screening, diagnosis, and staging of precancerous conditions (atrophy and intestinal metaplasia) and lesions (dysplasia or cancer), as well as after endoscopic therapy. VCE should be used to guide the sampling site for biopsies in the case of suspected neoplastic lesions as well as to guide biopsies for diagnosis and staging of gastric precancerous conditions, with random biopsies to be taken in the absence of endoscopically suspected changes. When there is a suspected early gastric neoplastic lesion, it should be properly described (location, size, Paris classification, vascular and mucosal pattern), photodocumented, and two targeted biopsies taken.ESGE/EHMSG/ESP do not recommend routine performance of endoscopic ultrasonography (EUS), computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET)-CT prior to endoscopic resection unless there are signs of deep submucosal invasion or if the lesion is not considered suitable for endoscopic resection.ESGE/EHMSG/ESP recommend endoscopic submucosal dissection (ESD) for differentiated gastric lesions clinically staged as dysplastic (low grade and high grade) or as intramucosal carcinoma (of any size if not ulcerated or ≤ 30 mm if ulcerated), with EMR being an alternative for Paris 0-IIa lesions of size ≤ 10 mm with low likelihood of malignancy.ESGE/EHMSG/ESP suggest that a decision about ESD can be considered for malignant lesions clinically staged as having minimal submucosal invasion if differentiated and ≤ 30 mm; or for malignant lesions clinically staged as intramucosal, undifferentiated and ≤ 20 mm; and in both cases with no ulcerative findings.ESGE/EHMSG/ESP recommends patient management based on the following histological risk after endoscopic resection: Curative/very low-risk resection (lymph node metastasis [LNM] risk < 0.5 %-1 %): en bloc R0 resection; dysplastic/pT1a, differentiated lesion, no lymphovascular invasion, independent of size if no ulceration and ≤ 30 mm if ulcerated. No further staging procedure or treatment is recommended.Curative/low-risk resection (LNM risk < 3 %): en bloc R0 resection; lesion with no lymphovascular invasion and: a) pT1b, invasion ≤ 500 µm, differentiated, size ≤ 30 mm; or b) pT1a, undifferentiated, size ≤ 20 mm and no ulceration. Staging should be completed, and further treatment is generally not necessary, but a multidisciplinary discussion is required. Local-risk resection (very low risk of LNM but increased risk of local persistence/recurrence): Piecemeal resection or tumor-positive horizontal margin of a lesion otherwise meeting curative/very low-risk criteria (or meeting low-risk criteria provided that there is no submucosal invasive tumor at the resection margin in the case of piecemeal resection or tumor-positive horizontal margin for pT1b lesions [invasion ≤ 500 µm; well-differentiated; size ≤ 30 mm, and VM0]). Endoscopic surveillance/re-treatment is recommended rather than other additional treatment. High-risk resection (noncurative): Any lesion with any of the following: (a) a positive vertical margin (if carcinoma) or lymphovascular invasion or deep submucosal invasion (> 500 µm from the muscularis mucosae); (b) poorly differentiated lesions if ulceration or size > 20 mm; (c) pT1b differentiated lesions with submucosal invasion ≤ 500 µm with size > 30 mm; or (d) intramucosal ulcerative lesion with size > 30 mm. Complete staging and strong consideration for additional treatments (surgery) in multidisciplinary discussion.ESGE/EHMSG/ESP suggest the use of validated endoscopic classifications of atrophy (e. g. Kimura-Takemoto) or intestinal metaplasia (e. g. endoscopic grading of gastric intestinal metaplasia [EGGIM]) to endoscopically stage precancerous conditions and stratify the risk for gastric cancer.ESGE/EHMSG/ESP recommend that biopsies should be taken from at least two topographic sites (2 biopsies from the antrum/incisura and 2 from the corpus, guided by VCE) in two separate, clearly labeled vials. Additional biopsy from the incisura is optional.ESGE/EHMSG/ESP recommend that patients with extensive endoscopic changes (Kimura C3 + or EGGIM 5 +) or advanced histological stages of atrophic gastritis (severe atrophic changes or intestinal metaplasia, or changes in both antrum and corpus, operative link on gastritis assessment/operative link on gastric intestinal metaplasia [OLGA/OLGIM] III/IV) should be followed up with high quality endoscopy every 3 years, irrespective of the individual's country of origin.ESGE/EHMSG/ESP recommend that no surveillance is proposed for patients with mild to moderate atrophy or intestinal metaplasia restricted to the antrum, in the absence of endoscopic signs of extensive lesions or other risk factors (family history, incomplete intestinal metaplasia, persistent H. pylori infection). This group constitutes most individuals found in clinical practice.ESGE/EHMSG/ESP recommend H. pylori eradication for patients with precancerous conditions and after endoscopic or surgical therapy.ESGE/EHMSG/ESP recommend that patients should be advised to stop smoking and low-dose daily aspirin use may be considered for the prevention of gastric cancer in selected individuals with high risk for cardiovascular events.

Artificial intelligence is rapidly transforming quality assurance in healthcare, driving advancements in diagnostics, surgery, and patient care. This review presents a comprehensive analysis of artificial intelligence integration-particularly convolutional and recurrent neural networks-across key clinical domains, significantly enhancing diagnostic accuracy, surgical performance, and pathology evaluation. Artificial intelligence-based approaches have demonstrated clear superiority over conventional methods: convolutional neural networks achieved 91.56% accuracy in scanner fault detection, surpassing manual inspections; endoscopic lesion detection sensitivity rose from 2.3% to 6.1% with artificial intelligence assistance; and gastric cancer invasion depth classification reached 89.16% accuracy, outperforming human endoscopists by 17.25%. In pathology, artificial intelligence achieved 93.2% accuracy in identifying out-of-focus regions and an F1 score of 0.94 in lymphocyte quantification, promoting faster and more reliable diagnostics. Similarly, artificial intelligence improved surgical workflow recognition with over 81% accuracy and exceeded 95% accuracy in skill assessment classification. Beyond traditional diagnostics and surgical support, AI-powered wearable sensors, drug delivery systems, and biointegrated devices are advancing personalized treatment by optimizing physiological monitoring, automating care protocols, and enhancing therapeutic precision. Despite these achievements, challenges remain in areas such as data standardization, ethical governance, and model generalizability. Overall, the findings underscore artificial intelligence's potential to outperform traditional techniques across multiple parameters, emphasizing the need for continued development, rigorous clinical validation, and interdisciplinary collaboration to fully realize its role in precision medicine and patient safety.

Gastric intestinal metaplasia (GIM) is a precancerous lesion and the key risk factor in the development of gastric cancer (GC), but early detection and treatment remain challenging. The traditional endoscopic diagnosis of metaplastic lesions is complicated by an increased rate of inappropriateness and false negativity. Although early interventions with H. pylori eradication, as well as endoscopic therapy results, were promising, there is still a significant unmet need to control GIM progression and recurrences. Molecular alterations, such as an increased DNA methylation index, have been identified as a crucial factor in the downregulation of tumor suppressor genes, such as the caudal-type homeobox (CDX2) gene, which regulates epithelial cell proliferation and GIM progression and is associated with treatment failure. CDX2 is downregulated by promoter hypermethylation in the colonic-type epithelium, in which the methylation was correlated with reduced intake of dietary folate sources. Tumor cells alter to dietary methionine sources in the biosynthesis of S-Adenosylmethionine, a universal methyl donor for transmethylation, under the conditions of limited folate and B12 availability. The gut microbiota also exhibited a shift in microbial composition, which could influence the host's dietary methionine metabolism. Meanwhile, activated oncogenic signaling via the PI3K/Akt/mTORC1/c-MYC pathway could promotes rewiring dietary methionine and cellular proliferation. Tumor methionine dependence is a metabolic phenotype that could be helpful in predictive screening of tumorigenesis and as a target for preventive therapy to enhance precision oncology. This review aimed to discuss the molecular alterations in GIM to shed light on the alteration of methionine metabolism, with insight into new diagnostic and treatment approaches and future research directions.

Esophageal cancer is one of the most common cancers worldwide, especially esophageal squamous cell carcinoma, which is often diagnosed at a late stage and has a poor prognosis. This study aimed to develop an algorithm to detect tumors in esophageal endoscopy images using innovative artificial intelligence (AI) techniques for early diagnosis and detection of esophageal cancer. We used white light and narrowband imaging data collected from Gachon University Gil Hospital, and applied YOLOv5 and RetinaNet detection models to detect lesions. The models demonstrated high performance, with RetinaNet achieving a precision of 98.4% and sensitivity of 91.3% in the NBI dataset, and YOLOv5 attaining a precision of 93.7% and sensitivity of 89.9% in the WLI dataset. The generalizability of these models was further validated using external data from multiple institutions. This study demonstrates an effective method for detecting esophageal tumors through AI-based esophageal endoscopic image analysis. These efforts are expected to significantly reduce misdiagnosis rates, enhance the effective diagnosis and treatment of esophageal cancer, and promote the standardization of medical services.

White light imaging (WLI) can sometimes miss early upper gastrointestinal (UGI) neoplasms, particularly minimal changes and flat lesions. Moreover, endoscopic diagnosis of UGI neoplasia is strongly influenced by the condition of the surrounding mucosa. Recently, image-enhanced endoscopy techniques have been developed and used in clinical practice; one of which is linked color imaging (LCI), which has an expanded color range for better recognition of slight differences in mucosal color and enables easy diagnosis and differentiation of noncancerous mucosa from carcinoma. LCI does not require magnified observation and can clearly visualize structures using an ultrathin scope; therefore, it is useful for screening and surveillance endoscopy. LCI is particularly useful for detecting gastric cancer after Helicobacter pylori eradication, which accounts for most gastric cancers currently discovered, and displays malignant areas in orange or orange-red surrounded by intestinal metaplasia in lavender. Data on the use of convolutional neural network and computer-aided diagnosis with LCI for UGI neoplasm detection are currently being collected. Further studies are needed to determine the clinical role of LCI and whether it can replace WLI.

The advent of artificial intelligence (AI) and deep learning algorithms, particularly convolutional neural networks, promises to address pitfalls, bridging the care for patients at high risk with improved detection (computer-aided detection [CADe]) and characterization (computer-aided diagnosis [CADx]) of lesions. This review describes the available artificial intelligence (AI) technology and the current data on AI tools for screening esophageal squamous cell cancer, Barret's esophagus-related neoplasia, and gastric cancer. These tools outperformed endoscopists in many situations. Recent randomized controlled trials have demonstrated the successful application of AI tools in clinical practice with improved outcomes.

Railroad construction sites are high-risk environments where monitoring personnel safety is critical for preventing accidents and enhancing construction efficiency. Traditional manual monitoring and image processing methods exhibit deficiencies in real-time performance and accuracy. This paper proposes a railway worker detection method based on improved support vector machines (ISVM), while using non-local mean noise reduction and histogram equalisation pre-processing techniques to optimise image quality to improve detection efficiency and accuracy. Multiscale features are then extracted with Inception v3 and combined with principal component analysis (PCA) for dimensionality reduction. Finally, an SVM classification algorithm is employed for personnel detection. To process small sample categories, data enhancement techniques (e.g., random flip and rotation) and K-fold cross-validation are applied to optimize the model parameters. The experimental results demonstrate that the ISVM method significantly improves accuracy and real-time performance compared to traditional detection methods and single deep learning models. This method provides technical support for railroad construction safety monitoring and effectively addresses personnel detection tasks in complex construction environments.

Nowadays, gastric cancer has become a significant issue in the global cancer burden, and its impact cannot be ignored. The rapid development of artificial intelligence technology is attempting to address this situation, aiming to change the clinical management landscape of gastric cancer fundamentally. In this transformative change, machine learning and deep learning, as two core technologies, play a pivotal role, bringing unprecedented innovations and breakthroughs in the diagnosis, treatment, and prognosis evaluation of gastric cancer. This article comprehensively reviews the latest research status and application of artificial intelligence algorithms in gastric cancer, covering multiple dimensions such as image recognition, pathological analysis, personalized treatment, and prognosis risk assessment. These applications not only significantly improve the sensitivity of gastric cancer risk monitoring, the accuracy of diagnosis, and the precision of survival prognosis but also provide robust data support and a scientific basis for clinical decision-making. The integration of artificial intelligence, from optimizing the diagnosis process and enhancing diagnostic efficiency to promoting the practice of precision medicine, demonstrates its promising prospects for reshaping the treatment model of gastric cancer. Although most of the current AI-based models have not been widely used in clinical practice, with the continuous deepening and expansion of precision medicine, we have reason to believe that a new era of AI-driven gastric cancer care is approaching.

Artificial intelligence (AI) holds the potential to transform the management of upper gastrointestinal (GI) conditions, such as Barrett's esophagus, esophageal squamous cell cancer, and early gastric cancer. Advancements in deep learning and convolutional neural networks offer improved diagnostic accuracy and reduced diagnostic variability across different clinical settings, particularly where human error or fatigue may impair diagnostic precision. Deep learning models have shown the potential to improve early cancer detection and lesion characterization, predict invasion depth, and delineate lesion margins with remarkable accuracy, all contributing to effective treatment planning. Several challenges, however, limit the broad application of AI in GI endoscopy, particularly in the upper GI tract. Subtle lesion morphology and restricted diversity in training datasets, which are often sourced from specialized centers, may constrain the generalizability of AI models in various clinical settings. Furthermore, the "black box" nature of some AI systems can impede explainability and clinician trust. To address these issues, efforts are underway to incorporate multimodal data, such as combining endoscopic and histopathologic imaging, to bolster model robustness and transparency. In the future, AI promises substantial advancements in automated real-time endoscopic guidance, personalized risk assessment, and optimized biopsy decision making. As it evolves, it would substantially impact not only early diagnosis and prognosis, but also the cost-effectiveness of managing upper GI diseases, ultimately leading to improved patient outcomes and more efficient health care delivery.

Endoscopic diagnosis of early gastric cancer (EGC) is a challenge. It is not clear whether deep convolutional neural network (DCNN) model could improve the endoscopists' diagnostic performance.

We established a DCNN-assisted system and found that accuracy of diagnosis is higher than endoscopists. We prospectively collected an independent image test set of 1289 images and a video test set of 130 patients from three endoscopic centers to compare the diagnostic efficacy of 12 endoscopists before and after DCNN model assistance. Accuracy, sensitivity, specificity, time, and AUC were the main indicators for comparison.

The DCNN model discriminated EGC from the control group (including ulcers and chronic gastritis) with an AUC of 0.917, a sensitivity of 93.38% (95% CI 91.09-95.12%), and a specificity of 90.07% (95% CI 87.59-92.10%) in the image dataset. The video test dataset have an AUC of 0.930, a sensitivity of 96.92% (95% CI 88.83-99.78%), and a specificity of 89.23% (95% CI 79.11-94.98%). The diagnostic performance of novice endoscopists was comparable to those of expert endoscopists with the DCNN model's assistance (accuracy: 95.22 vs. 96.16%) in image test dataset. In the video test, the novice endoscopists, accuracy after DCNN assistance was also improved from 79.36 to 86.41%, and from 86.28 to 91.03% for expert endoscopists. The mean pairwise kappa value of endoscopists was increased significantly with the DCNN model's assistance (0.705-0.753 vs.0.767-0.890) in image testing, and (0.657-0.793 vs. 0.738-0.905) in video testing. The diagnostic duration reduced considerably with the assistance of the DCNN model from 7.09 ± 0.6 s to 5.05 ± 0.55 s in image test, and from 2392.17 ± 7.77 s to2378.34 ± 23.51 s in video test.

We developed a DCNN-assisted diagnostic system. And the system can improve the diagnostic performance of endoscopists and help novice endoscopists achieve diagnostic accuracy comparable to that of expert endoscopists.

This study aims to address the diagnostic challenges in distinguishing gastric polyps from protrusions, emphasizing the need for accurate and cost-effective diagnosis strategies. It explores the application of Convolutional Neural Networks (CNNs) to improve diagnostic accuracy. This research introduces MultiAttentiveScopeNet, a deep learning model that incorporates multi-layer feature ensemble and attention mechanisms to enhance gastroscopy image analysis accuracy. A weakly supervised labeling strategy was employed to construct a large multi-class gastroscopy image dataset for training and validation. MultiAttentiveScopeNet demonstrates significant improvements in prediction accuracy and interpretability. The integrated attention mechanism effectively identifies critical areas in images to aid clinical decisions. Its multi-layer feature ensemble enables robust analysis of complex gastroscopy images. Comparative testing against human experts shows exceptional diagnostic performance, with accuracy, micro and macro precision, micro and macro recall, and micro and macro AUC reaching 0.9308, 0.9312, 0.9325, 0.9283, 0.9308, 0.9847 and 0.9853 respectively. This highlights its potential as an effective tool for primary healthcare settings. This study provides a comprehensive solution to address diagnostic challenges differentiating gastric polyps and protrusions. MultiAttentiveScopeNet improves accuracy and interpretability, demonstrating the potential of deep learning for gastroscopy image analysis. The constructed dataset facilitates continued model optimization and validation. The model shows promise in enhancing diagnostic outcomes in primary care.

Devices that help educate young doctors and enable safe, minimally invasive surgery are needed. Eureka is a surgical artificial intelligence (AI) system that can intraoperatively highlight loose connective tissues (LCTs) in the dissected layers and nerves in the surgical field displayed on a monitor. In this study, we examined whether AI navigation (AIN) with Eureka can assist trainees in recognizing nerves during colorectal surgery.

In left-sided colorectal surgery (n = 51, between July 2023 and February 2024), Eureka was connected to the laparoscopic system side by side, and the nerve was highlighted on the monitor during the surgery. We examined the rate of failure to recognize nerves by trainee surgeons over a total of 101 scenarios after it was recognized intraoperatively by the supervising surgeon (certified by the Japanese Society of Endoscopic Surgery). We also examined the frequency of nerve recognition by the trainee physicians viewing the Eureka monitor when recognition was not possible (recognition assistance rate).

The nerve recognition failure rate and recognition assistance rate with AIN were as follows: right hypogastric nerve during sigmoid colon mobilization, 44/101 (43.6%) and 19/44 (43.2%); left hypogastric nerves during dissection of the dorsal rectum, 27/101 (26.7%) and 13/27 (48.1%); right lumbar splanchnic nerves, 32/101 (31.7%) and 29/32 (90.6%); left lumbar splanchnic nerves, 44/101 (43.6%) and 39/44 (88.6%); and pelvic visceral nerves during dissection of the dorsal rectum, 29/45 (64.4%) and 6/29 (20.7%), respectively.

Although the rate of recognition with assistance from AIN differed for the different nerves, this system can potentially assist in anatomic recognition, enhance surgical education, and contribute to nerve preservation.

Improvement of AI navigation in minimally invasive surgery and examination of its intraoperative support and educational effectiveness. Research Ethics Committee of the Kawaguchi Municipal Medical Center (Saitama, Japan) approval number: 2022-27.

Recent advancements in deep learning have significantly improved the intelligent classification of gastrointestinal (GI) diseases, particularly in aiding clinical diagnosis. This paper seeks to review a computer-aided diagnosis (CAD) system for GI diseases, aligning with the actual clinical diagnostic process. It offers a comprehensive survey of deep learning (DL) techniques tailored for classifying GI diseases, addressing challenges inherent in complex scenes, clinical constraints, and technical obstacles encountered in GI imaging. Firstly, the esophagus, stomach, small intestine, and large intestine were located to determine the organs where the lesions were located. Secondly, location detection and classification of a single disease are performed on the premise that the organ's location corresponding to the image is known. Finally, comprehensive classification for multiple diseases is carried out. The results of single and multi-classification are compared to achieve more accurate classification outcomes, and a more effective computer-aided diagnosis system for gastrointestinal diseases was further constructed.

Gastric cancer is a major cause of cancer-related death worldwide, despite the reduction in its incidence. The disease is still burdened with a poor prognosis, particularly in Western countries. The main risk factor is the infection by Helicobacter pylori, classified as a class I carcinogen by the IARC, and It is well-known that primary prevention of gastric cancer can be achieved with the eradication of the infection. Moreover, non-invasive measurement of pepsinogens (PGI and PGI/PGII ratio) allows the identification of patients that should undergo upper gastrointestinal (GI) endoscopy. Gastric non-cardia adenocarcinoma is indeed preceded by a well-defined precancerous process that involves consecutive stages, described for the first time by Correa et al. more than 40 years ago, and patients with advance stages of gastric atrophy/intestinal metaplasia and with dysplastic changes should be followed-up periodically with upper GI endoscopies. Despite these effective screening and surveillance methods, national-level screening campaigns have been adopted only in few countries in eastern Asia (Japan and South Korea). In this review, we describe primary and secondary preventive measures for gastric cancer, discussing the need to introduce screening also in Western countries. Moreover, we propose a simple algorithm for screening that could be easily applied in clinical practice.

Esophagogastroduodenoscopy is the most important tool to detect gastric cancer (GC). In this study, we developed a computer-aided detection (CADe) system to detect GC with white light imaging (WLI) and linked color imaging (LCI) modes and aimed to compare the performance of CADe with that of endoscopists.

The system was developed based on the deep learning framework from 9,021 images in 385 patients between 2017 and 2020. A total of 116 LCI and WLI videos from 110 patients between 2017 and 2023 were used to evaluate per-case sensitivity and per-frame specificity.

The per-case sensitivity and per-frame specificity of CADe with a confidence level of 0.5 in detecting GC were 78.6% and 93.4% for WLI and 94.0% and 93.3% for LCI, respectively (p < 0.001). The per-case sensitivities of nonexpert endoscopists for WLI and LCI were 45.8% and 80.4%, whereas those of expert endoscopists were 66.7% and 90.6%, respectively. Regarding detectability between CADe and endoscopists, the per-case sensitivities for WLI and LCI were 78.6% and 94.0% in CADe, respectively, which were significantly higher than those for LCI in experts (90.6%, p = 0.004) and those for WLI and LCI in nonexperts (45.8% and 80.4%, respectively, p < 0.001); however, no significant difference for WLI was observed between CADe and experts (p = 0.134).

Our CADe system showed significantly better sensitivity in detecting GC when used in LCI compared with WLI mode. Moreover, the sensitivity of CADe using LCI is significantly higher than those of expert endoscopists using LCI to detect GC.

Artificial intelligence (AI) using deep learning systems has recently been utilized in various medical fields. In the field of gastroenterology, AI is primarily implemented in image recognition and utilized in the realm of gastrointestinal (GI) endoscopy. In GI endoscopy, computer-aided detection/diagnosis (CAD) systems assist endoscopists in GI neoplasm detection or differentiation of cancerous or noncancerous lesions. Several AI systems for colorectal polyps have already been applied in colonoscopy clinical practices. In esophagogastroduodenoscopy, a few CAD systems for upper GI neoplasms have been launched in Asian countries. The usefulness of these CAD systems in GI endoscopy has been gradually elucidated.

In this review, we outline recent articles on several studies of endoscopic AI systems for GI neoplasms, focusing on esophageal squamous cell carcinoma (ESCC), esophageal adenocarcinoma (EAC), gastric cancer (GC), and colorectal polyps. In ESCC and EAC, computer-aided detection (CADe) systems were mainly developed, and a recent meta-analysis study showed sensitivities of 91.2% and 93.1% and specificities of 80% and 86.9%, respectively. In GC, a recent meta-analysis study on CADe systems demonstrated that their sensitivity and specificity were as high as 90%. A randomized controlled trial (RCT) also showed that the use of the CADe system reduced the miss rate. Regarding computer-aided diagnosis (CADx) systems for GC, although RCTs have not yet been conducted, most studies have demonstrated expert-level performance. In colorectal polyps, multiple RCTs have shown the usefulness of the CADe system for improving the polyp detection rate, and several CADx systems have been shown to have high accuracy in colorectal polyp differentiation.

Most analyses of endoscopic AI systems suggested that their performance was better than that of nonexpert endoscopists and equivalent to that of expert endoscopists. Thus, endoscopic AI systems may be useful for reducing the risk of overlooking lesions and improving the diagnostic ability of endoscopists.

Gastric intestinal metaplasia is a precancerous disease, and a timely diagnosis is essential to delay or halt cancer progression. Artificial intelligence (AI) has found widespread application in the field of disease diagnosis. This study aimed to conduct a comprehensive evaluation of AI's diagnostic accuracy in detecting gastric intestinal metaplasia in endoscopy, compare it to endoscopists' ability, and explore the main factors affecting AI's performance.

The study followed the PRISMA-DTA guidelines, and the PubMed, Embase, Web of Science, Cochrane, and IEEE Xplore databases were searched to include relevant studies published by October 2023. We extracted the key features and experimental data of each study and combined the sensitivity and specificity metrics by meta-analysis. We then compared the diagnostic ability of the AI versus the endoscopists using the same test data.

Twelve studies with 11,173 patients were included, demonstrating AI models' efficacy in diagnosing gastric intestinal metaplasia. The meta-analysis yielded a pooled sensitivity of 94% (95% confidence interval: 0.92-0.96) and specificity of 93% (95% confidence interval: 0.89-0.95). The combined area under the receiver operating characteristics curve was 0.97. The results of meta-regression and subgroup analysis showed that factors such as study design, endoscopy type, number of training images, and algorithm had a significant effect on the diagnostic performance of AI. The AI exhibited a higher diagnostic capacity than endoscopists (sensitivity: 95% vs. 79%).

AI-aided diagnosis of gastric intestinal metaplasia using endoscopy showed high performance and clinical diagnostic value. However, further prospective studies are required to validate these findings.

The Japan Gastroenterological Endoscopy Society (JGES) held four serial symposia between 2021 and 2022 on state-of-the-art issues related to advanced diagnostic endoscopy of the upper gastrointestinal tract. This review summarizes the four core sessions and presents them as a conference report. Eleven studies were discussed in the 101st JGES Core Session, which addressed the challenges and prospects of upper gastroenterological endoscopy. Ten studies were also explored in the 102nd JGES Core Session on advanced upper gastrointestinal endoscopic diagnosis for decision-making regarding therapeutic strategies. Moreover, eight studies were presented during the 103rd JGES Core Session on the development and evaluation of endoscopic artificial intelligence in the field of upper gastrointestinal endoscopy. Twelve studies were also discussed in the 104th JGES Core Session, which focused on the evidence and new developments related to the upper gastrointestinal tract. The endoscopic diagnosis of upper gastrointestinal diseases using image-enhanced endoscopy and AI is one of the most recent topics and has received considerable attention. These four core sessions enabled us to grasp the current state-of-the-art in upper gastrointestinal endoscopic diagnostics and identify future challenges. Based on these studies, we hope that an endoscopic diagnostic system useful in clinical practice is established for each field of upper gastrointestinal endoscopy.

The introduction of artificial intelligence into the medical field has improved the diagnostic capabilities of physicians. However, few studies have analyzed the economic impact of employing artificial intelligence technologies in the clinical environment. This study evaluated the cost-effectiveness of a computer-assisted diagnosis (CADx) system designed to support clinicians in differentiating early gastric cancers from non-cancerous lesions in Japan, where the universal health insurance system was introduced.

The target population to be used for the CADx was estimated as those with moderate to severe gastritis caused by Helicobacter pylori infection. Decision trees with Markov models were built to analyze the cumulative cost-effectiveness of using CADx relative to the pre-artificial intelligence status quo, a condition reconstructed from data in published reports. After conducting a base-case analysis, we performed sensitivity analyses by modifying several parameters. The primary outcome was the incremental cost-effectiveness ratio.

Compared with the status quo as represented in the base-case analysis, the incremental cost-effectiveness ratio of CADx in the Japanese market was forecasted to be 11,093 USD per quality-adjusted life year. The sensitivity analyses demonstrated that the expected incremental cost-effectiveness ratios were within the willingness-to-pay threshold of 50,000 USD per quality-adjusted life year when the cost of the CAD was less than 104 USD.

Using CADx for EGCs may decrease their misdiagnosis, contributing to improved cost-effectiveness in Japan.

The application of machine learning (ML) for identifying early gastric cancer (EGC) has drawn increasing attention. However, there lacks evidence-based support for its specific diagnostic performance. Hence, this systematic review and meta-analysis was implemented to assess the performance of image-based ML in EGC diagnosis.

We performed a comprehensive electronic search in PubMed, Embase, Cochrane Library, and Web of Science up to September 25, 2022. QUADAS-2 was selected to judge the risk of bias of included articles. We did the meta-analysis using a bivariant mixed-effect model. Sensitivity analysis and heterogeneity test were performed.

Twenty-one articles were enrolled. The sensitivity (SEN), specificity (SPE), and SROC of ML-based models were 0.91 (95% CI: 0.87-0.94), 0.85 (95% CI: 0.81-0.89), and 0.94 (95% CI: 0.39-1.00) in the training set and 0.90 (95% CI: 0.86-0.93), 0.90 (95% CI: 0.86-0.92), and 0.96 (95% CI: 0.19-1.00) in the validation set. The SEN, SPE, and SROC of EGC diagnosis by non-specialist clinicians were 0.64 (95% CI: 0.56-0.71), 0.84 (95% CI: 0.77-0.89), and 0.80 (95% CI: 0.29-0.97), and those by specialist clinicians were 0.80 (95% CI: 0.74-0.85), 0.88 (95% CI: 0.85-0.91), and 0.91 (95% CI: 0.37-0.99). With the assistance of ML models, the SEN of non-specialist physicians in the diagnosis of EGC was significantly improved (0.76 vs 0.64).

ML-based diagnostic models have greater performance in the identification of EGC. The diagnostic accuracy of non-specialist clinicians can be improved to the level of the specialists with the assistance of ML models. The results suggest that ML models can better assist less experienced clinicians in diagnosing EGC under endoscopy and have broad clinical application value.

Most of the development of gastric disease prediction models has utilized pre-trained models from natural data, such as ImageNet, which lack knowledge of medical domains. This study proposes Gastro-BaseNet, a classification model trained using gastroscopic image data for abnormal gastric lesions. To prove performance, we compared transfer-learning based on two pre-trained models (Gastro-BaseNet and ImageNet) and two training methods (freeze and fine-tune modes). The effectiveness was verified in terms of classification at the image-level and patient-level, as well as the localization performance of lesions. The development of Gastro-BaseNet had demonstrated superior transfer learning performance compared to random weight settings in ImageNet. When developing a model for predicting the diagnosis of gastric cancer and gastric ulcers, the transfer-learned model based on Gastro-BaseNet outperformed that based on ImageNet. Furthermore, the model's performance was highest when fine-tuning the entire layer in the fine-tune mode. Additionally, the trained model was based on Gastro-BaseNet, which showed higher localization performance, which confirmed its accurate detection and classification of lesions in specific locations. This study represents a notable advancement in the development of image analysis models within the medical field, resulting in improved diagnostic predictive accuracy and aiding in making more informed clinical decisions in gastrointestinal endoscopy.

Gastric cancer (GC), a significant health burden worldwide, is typically diagnosed in the advanced stages due to its non-specific symptoms and complex morphological features. Deep learning (DL) has shown potential for improving and standardizing early GC detection. This systematic review aims to evaluate the current status of DL in pre-malignant, early-stage, and gastric neoplasia analysis.

A comprehensive literature search was conducted in PubMed/MEDLINE for original studies implementing DL algorithms for gastric neoplasia detection using endoscopic images. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The focus was on studies providing quantitative diagnostic performance measures and those comparing AI performance with human endoscopists.

Our review encompasses 42 studies that utilize a variety of DL techniques. The findings demonstrate the utility of DL in GC classification, detection, tumor invasion depth assessment, cancer margin delineation, lesion segmentation, and detection of early-stage and pre-malignant lesions. Notably, DL models frequently matched or outperformed human endoscopists in diagnostic accuracy. However, heterogeneity in DL algorithms, imaging techniques, and study designs precluded a definitive conclusion about the best algorithmic approach.

The promise of artificial intelligence in improving and standardizing gastric neoplasia detection, diagnosis, and segmentation is significant. This review is limited by predominantly single-center studies and undisclosed datasets used in AI training, impacting generalizability and demographic representation. Further, retrospective algorithm training may not reflect actual clinical performance, and a lack of model details hinders replication efforts. More research is needed to substantiate these findings, including larger-scale multi-center studies, prospective clinical trials, and comprehensive technical reporting of DL algorithms and datasets, particularly regarding the heterogeneity in DL algorithms and study designs.

Gastric cancer, ranked as the fifth most common cancer worldwide, presents multiple treatment challenges. These obstacles often arise due to cancer stem cells, which are associated with recurrence, metastasis, and drug resistance. While dendritic cell (DC)-based immunotherapy has shown promise as a therapeutic strategy, its efficacy can be limited by the tumor microenvironment and certain inhibitory immune checkpoint molecules, such as B7H7. SiRNA-medicated knockdown of B7H7 in tumor cell lysate-pulsed DCs can increase cytokine secretion and autologous T lymphocyte expansion. This study aimed to evaluate the impact of B7H7 suppression in gastric cancer cell lysate-pulsed DCs on the stimulatory potential of autologous CD3+ T lymphocytes.

Peripheral blood mononuclear cells (PBMCs) were isolated and monocytes were obtained; then, they were differentiated to immature DCs (iDCs) by GM-CSF and IL-4. Tumor cell lysates from human gastric cancer cell lines were harvested, and iDCs were transformed into mature DCs (mDCs) by stimulating iDCs with tumor cell lysate and lipopolysaccharide. B7H7-siRNA was delivered into mDCs using electroporation, and gene silencing efficiency was assessed. The phenotypic characteristics of iDCs, mDCs, and B7H7-silenced mDCs were evaluated using specific surface markers, an inverted light microscope, and flow cytometry. CD3+ T cells were isolated via magnetically activated cell sorting. They were labeled with CFSE dye and co-cultured with mDCs and B7H7-silenced mDCs to evaluate their ability to induce T-cell proliferation. T-cell proliferation was assessed using flow cytometry. The concentration of TGF-β, IL-4, and IFN-γ secreted from CD3+ T cells in the co-cultured supernatant was evaluated to investigate the cytokine secretory activity of the cells.

Transfection of B7H7 siRNA into mDCs was performed in optimal conditions, and the siRNA transfection effectively reduced B7H7 mRNA expression in a dose-dependent manner. SiRNA-mediated B7H7 knockdown in mDCs enhanced maturation and activation of the DCs, as demonstrated by an increased surface expression of CD11c, CD86, and CD40. Co-culture experiments revealed that B7H7-silenced mDCs had more capacity to induce T cell proliferation compared to non-transfected mDCs. The cytokine production patterns of T cells were also altered. Upon examining the levels of TGF-β, IL-4, and IFN-γ released by CD3+ T cells in the co-culture supernatant, we found that silencing B7H7 in mDCs resulted in a rise in IL-4 secretion and a reduction in TGF-β levels compared to mDCs that were not transfected.

The study found that suppressing B7H7 expression in DCs significantly enhances their maturation and stimulatory activity when exposed to gastric cancer cell lysate. These B7H7-silenced DCs can substantially increase cytokine production and promote co-cultured T-cell expansion. Consequently, inhibiting B7H7 in DCs may offer a practical strategy to enhance the ability of DCs to initiate T lymphocyte responses and improve the effectiveness of DC-based cell therapy for cancer patients.

Because endoscopic submucosal dissection (ESD) for early gastric cancer (EGC) preserves the entire stomach, missed gastric cancers (MGCs) are often found in the remaining gastric mucosa. However, the endoscopic causes of MGCs remain unclear. Therefore, we aimed to elucidate the endoscopic causes and characteristics of MGCs after ESD.

From January 2009 to December 2018, all patients undergoing ESD for initially detected EGC were enrolled. According to a review of EGD images before ESD, we identified the endoscopic causes (perceptual, exposure, sampling errors, and inadequate preparation) and characteristics of MGC in each endoscopic cause.

Of 2208 patients who underwent ESD for initial EGC, 82 patients (3.7%) had 100 MGCs. The breakdown of endoscopic causes of MGCs was as follows: 69 (69%) perceptual errors, 23 (23%) exposure errors, 7 (7%) sampling errors, and 1 (1%) inadequate preparation. Logistic regression analysis showed that the risk factors for perceptual error were male sex (odds ratio [OR], 2.45; 95% confidence interval [CI], 1.16-5.18), isochromatic coloration (OR, 3.17; 95% CI, 1.47-6.84), greater curvature (OR, 2.31; 95% CI, 1.121-4.40), and lesion size ≤12 mm (OR, 1.74; 95% CI, 1.07-2.84). The sites of exposure errors were around the incisura angularis (11 [48%]), posterior wall of the gastric body (6 [26%]), and antrum (5 [21%]).

We identified MGCs in 4 categories and clarified their characteristics. Quality improvements in EGD observation, with attention to the risks of perceptual and site of exposure errors, can potentially prevent missing EGCs.

Recently, there has been a growing interest in the application of artificial intelligence (AI) in medicine, especially in specialties where visualization methods are applied. AI is defined as a computer's ability to achieve human cognitive performance, which is accomplished through enabling computer "learning". This can be conducted in two ways, as machine learning and deep learning. Deep learning is a complex learning system involving the application of artificial neural networks, whose algorithms imitate the human form of learning. Upper gastrointestinal endoscopy allows examination of the esophagus, stomach and duodenum. In addition to the quality of endoscopic equipment and patient preparation, the performance of upper endoscopy depends on the experience and knowledge of the endoscopist. The application of artificial intelligence in endoscopy refers to computer-aided detection and the more complex computer-aided diagnosis. The application of AI in upper endoscopy is aimed at improving the detection of premalignant and malignant lesions, with special attention on the early detection of dysplasia in Barrett's esophagus, the early detection of esophageal and stomach cancer and the detection of H. pylori infection. Artificial intelligence reduces the workload of endoscopists, is not influenced by human factors and increases the diagnostic accuracy and quality of endoscopic methods.

We held four upper gastrointestinal tract advanced diagnostic endoscopy sessions from the 89th to the 92nd Congress of the Japan Gastroenterological Endoscopy Society. The most common region addressed was the stomach in 25 presentations, followed by the esophagus in 23, duodenum in five, and other in one. Looking at techniques discussed, the most common image enhancement method discussed was narrowband imaging in 29 presentations, blue laser imaging, and linked color imaging (LCI) in 10 each, dual red imaging in three, and autofluorescence imaging in one. Furthermore, there were presentations of new techniques such as M-Chromo-LCI and acetic acid-indigo carmine mixture LCI. There were also six presentations regarding probe-based confocal laser endomicroscopy, and one of endocytoscopy techniques. We also saw presentations of images of gastric subepithelial tumors within the submucosa, 3D endoscopy, the development of computer-aided detection systems for early cancers, and fluorescent imaging.

Artificial intelligence (AI) uses deep learning functionalities that may enhance the detection of early gastric cancer during endoscopy. An AI-based endoscopic system for upper endoscopy was recently developed in Japan. We aim to validate this AI-based system in a Singaporean cohort.

There were 300 de-identified still images prepared from endoscopy video files obtained from subjects that underwent gastroscopy in National University Hospital (NUH). Five specialists and 6 non-specialists (trainees) from NUH were assigned to read and categorize the images into "neoplastic" or "non-neoplastic." Results were then compared with the readings performed by the endoscopic AI system.

The mean accuracy, sensitivity, and specificity for the 11 endoscopists were 0.847, 0.525, and 0.872, respectively. These values for the AI-based system were 0.777, 0.591, and 0.791, respectively. While AI in general did not perform better than endoscopists on the whole, in the subgroup of high-grade dysplastic lesions, only 29.1% were picked up by the endoscopist rating, but 80% were classified as neoplastic by AI (P = 0.0011). The average diagnostic time was also faster in AI compared with endoscopists (677.1 s vs 42.02 s (P < 0.001).

We demonstrated that an AI system developed in another health system was comparable in diagnostic accuracy in the evaluation of static images. AI systems are faster and not fatigable and may have a role in augmenting human diagnosis during endoscopy. With more advances in AI and larger studies to support its efficacy it would likely play a larger role in screening endoscopy in future.

Helicobacter pylori (H. pylori) infection is the principal cause of chronic gastritis, gastric ulcers, duodenal ulcers, and gastric cancer. In clinical practice, diagnosis of H. pylori infection by a gastroenterologists' impression of endoscopic images is inaccurate and cannot be used for the management of gastrointestinal diseases. The aim of this study was to develop an artificial intelligence classification system for the diagnosis of H. pylori infection by pre-processing endoscopic images and machine learning methods. Endoscopic images of the gastric body and antrum from 302 patients receiving endoscopy with confirmation of H. pylori status by a rapid urease test at An Nan Hospital were obtained for the derivation and validation of an artificial intelligence classification system. The H. pylori status was interpreted as positive or negative by Convolutional Neural Network (CNN) and Concurrent Spatial and Channel Squeeze and Excitation (scSE) network, combined with different classification models for deep learning of gastric images. The comprehensive assessment for H. pylori status by scSE-CatBoost classification models for both body and antrum images from same patients achieved an accuracy of 0.90, sensitivity of 1.00, specificity of 0.81, positive predictive value of 0.82, negative predicted value of 1.00, and area under the curve of 0.88. The data suggest that an artificial intelligence classification model using scSE-CatBoost deep learning for gastric endoscopic images can distinguish H. pylori status with good performance and is useful for the survey or diagnosis of H. pylori infection in clinical practice.

Stomach cancer has a high annual mortality rate worldwide necessitating early detection and accurate treatment. Even experienced specialists can make erroneous judgments based on several factors. Artificial intelligence (AI) technologies are being developed rapidly to assist in this field. Here, we aimed to determine how AI technology is used in gastric cancer diagnosis and analyze how it helps patients and surgeons. Early detection and correct treatment of early gastric cancer (EGC) can greatly increase survival rates. To determine this, it is important to accurately determine the diagnosis and depth of the lesion and the presence or absence of metastasis to the lymph nodes, and suggest an appropriate treatment method. The deep learning algorithm, which has learned gastric lesion endoscopyimages, morphological characteristics, and patient clinical information, detects gastric lesions with high accuracy, sensitivity, and specificity, and predicts morphological characteristics. Through this, AI assists the judgment of specialists to help select the correct treatment method among endoscopic procedures and radical resections and helps to predict the resection margins of lesions. Additionally, AI technology has increased the diagnostic rate of both relatively inexperienced and skilled endoscopic diagnosticians. However, there were limitations in the data used for learning, such as the amount of quantitatively insufficient data, retrospective study design, single-center design, and cases of non-various lesions. Nevertheless, this assisted endoscopic diagnosis technology that incorporates deep learning technology is sufficiently practical and future-oriented and can play an important role in suggesting accurate treatment plans to surgeons for resection of lesions in the treatment of EGC.

Gastric cancer (GC) is one of the commonest cancers with high morbidity and mortality in the world. How to realize precise diagnosis and therapy of GC owns great clinical requirement. In recent years, artificial intelligence (AI) has been actively explored to apply to early diagnosis and treatment and prognosis of gastric carcinoma. Herein, we review recent advance of AI in early screening, diagnosis, therapy and prognosis of stomach carcinoma. Especially AI combined with breath screening early GC system improved 97.4 % of early GC diagnosis ratio, AI model on stomach cancer diagnosis system of saliva biomarkers obtained an overall accuracy of 97.18 %, specificity of 97.44 %, and sensitivity of 96.88 %. We also discuss concept, issues, approaches and challenges of AI applied in stomach cancer. This review provides a comprehensive view and roadmap for readers working in this field, with the aim of pushing application of AI in theranostics of stomach cancer to increase the early discovery ratio and curative ratio of GC patients.

The development and clinical application of new diagnostic endoscopic technologies such as endoscopic ultrasonography with biopsy, magnification endoscopy, and narrow-band imaging, more recently supplemented by artificial intelligence, have enabled wider recognition and detection of various gastric neoplasms including early gastric cancer (EGC) and subepithelial tumors, such as gastrointestinal stromal tumors and neuroendocrine tumors. Over the last decade, the evolution of novel advanced therapeutic endoscopic techniques, such as endoscopic mucosal resection, endoscopic submucosal dissection, endoscopic full-thickness resection, and submucosal tunneling endoscopic resection, along with the advent of a broad array of endoscopic accessories, has provided a promising and yet less invasive strategy for treating gastric neoplasms with the advantage of a reduced need for gastric surgery. Thus, the management algorithms of various gastric tumors in a defined subset of the patient population at low risk of lymph node metastasis and amenable to endoscopic resection, may require revision considering upcoming data given the high success rate of en bloc resection by experienced endoscopists. Moreover, endoscopic surveillance protocols for precancerous gastric lesions will continue to be refined by systematic reviews and meta-analyses of further research. However, the lack of familiarity with subtle endoscopic changes associated with EGC, as well as longer procedural time, evolving resection techniques and tools, a steep learning curve of such high-risk procedures, and lack of coding are issues that do not appeal to many gastroenterologists in the field. This review summarizes recent advances in the endoscopic management of gastric neoplasms, with special emphasis on diagnostic and therapeutic methods and their future prospects.

White light endoscopy is the most pivotal tool for detecting early gastric neoplasms. Previous artificial intelligence (AI) systems were primarily unexplainable, affecting their clinical credibility and acceptability. We aimed to develop an explainable AI named ENDOANGEL-ED (explainable diagnosis) to solve this problem. A total of 4482 images and 296 videos with focal lesions from 3279 patients from eight hospitals were used for training, validating, and testing ENDOANGEL-ED. A traditional sole deep learning (DL) model was trained using the same dataset. The performance of ENDOANGEL-ED and sole DL was evaluated on six levels: internal and external images, internal and external videos, consecutive videos, and man-machine comparison with 77 endoscopists in videos. Furthermore, a multi-reader, multi-case study was conducted to evaluate the ENDOANGEL-ED's effectiveness. A scale was used to compare the overall acceptance of endoscopists to traditional and explainable AI systems. The ENDOANGEL-ED showed high performance in the image and video tests. In man-machine comparison, the accuracy of ENDOANGEL-ED was significantly higher than that of all endoscopists in internal (81.10% vs. 70.61%, p < 0.001) and external videos (88.24% vs. 78.49%, p < 0.001). With ENDOANGEL-ED's assistance, the accuracy of endoscopists significantly improved (70.61% vs. 79.63%, p < 0.001). Compared with the traditional AI, the explainable AI increased the endoscopists' trust and acceptance (4.42 vs. 3.74, p < 0.001; 4.52 vs. 4.00, p < 0.001). In conclusion, we developed a real-time explainable AI that showed high performance, higher clinical credibility, and acceptance than traditional DL models and greatly improved the diagnostic ability of endoscopists.