Esophageal cancer (EC) ranks seventh highest amongst malignancy occurrences worldwide, with 604000 new cases of incidence each year [1]. It has the sixth highest mortality rate, accounting for 544000 deaths per year [1]. The two most common histologic subtypes of EC, squamous cell carcinoma and adenocarcinoma, exhibit different geographic manifestations. Apart from the rapidly increasing global incidence rate of adenocarcinoma, squamous cell carcinoma has a high incidence rate in Eastern Asia [2,3]. With poor prognosis and high mortality rates, the early detection of EC is important for favorable disease outcomes.

As a precancerous condition, Barrett’s esophagus (BE) is characterized by the replacement of the natural squamous epithelium with intestinal differentiation of specialized epithelium. The development of BE is closely related to gastroesophageal reflux disease (GERD); however, its progression to cancerous conditions is independent of the presence of GERD.

Missed diagnosis with conventional white-light endoscopy (WLE) for early EC that is demonstrated as interval cancer is commonly encountered in clinical field [4,5]. Chromoendoscopy using Lugol’s solution is a standard screening tool for detecting early EC, but it has the limitations of causing chest discomfort for patients and prolongation of endoscopic procedure time for the endoscopist [6,7]. Progressive endoscopic imaging modalities including narrow band imaging (NBI) and magnification endoscopy have been applied in combination with WLE to enhance the endoscopic detection rate of early malignant lesion features in intrapapillary capillary loops (IPCLs) [8].

However, endoscopic detection of early EC is not easy as it depends on the endoscopists’ subjective decision and endoscopic technical expertise in detecting the delicate and fine microvascular change of esophageal mucosa [9].

Specifically, the rate of missed diagnosis for esophageal squamous cell carcinoma (ESCC) is 4%–17% [9].

Recently, artificial intelligence (AI) has been introduced into various medical fields to support and assist in diagnosis. This presents a promising possibility for meeting the requirements for endoscopic detection and diagnosis, including early EC [10,11].

Machine learning and deep learning are key factors of AI [12]. Among these, convolutional neural networks (CNNs), including ResNets, are most commonly used for image classification tasks, whereas object-detection frameworks such as You Only Look Once (YOLO) are optimized for real-time lesion localization. Encoder–decoder architectures with U-Net are frequently applied on segmentation and margin delineation, and more recent models such as Vision Transformers (ViTs) are currently being introduced. CNN-based models extract hierarchical features from images through multilayered convolutional operations, whereas deep learning detects convoluted structures from datasets using a backpropagation algorithm [13]. These architectures enable AI systems to function as supportive assistants by analyzing large-scale endoscopic and histopathological imaging datasets.

In this review article, we investigated the development of the current AI model introduced for esophageal precancerous lesions, including Barrett’s and EC.

The detection and identification of subtle structural changes in esophageal adenocarcinoma (EAC) associated with BE, which can be easily missed by human vision, are assisted by the application of AI. Improvement in the neoplasia detection rate (NDR) has contributed to the early diagnosis of EAC and advanced prognosis. A reduction of 3.5% in Barrett’s neoplasia was achieved by a 1% increase in the NDR [14]. There has been a shift from focusing on endoscopic still images to real-time systems for evaluating the model performance of AI, resulting in an abrupt progression in the capacity of AI computing systems. Most BE detection systems have deep CNN backbones for feature extraction in combination with detection heads or segmentation networks for lesion localization. Current algorithms are generated independently through manipulation by the operator, allowing the integrated output of AI to be presented as a region of interest surrounded by bounding boxes or heatmaps that act as active assistants during the endoscopic procedure. The goal of the final development and implementation of AI in the endoscopic clinical field is to remove and overcome the gap between preliminary validation results and real-world outcomes. Table 1 summarizes the current studies with AI application in BE and early Barrett’s neoplasia, including the imaging modality, AI architecture, study design, and diagnostic performance.

de Groof et al. [15] evaluated preliminary diagnostic accuracy of deep learning–based computer-aided detection (CADe) systems trained on high-resolution WLE images detecting early Barrett’s neoplasia. It had per-level accuracy of 90% and demonstrated significance of evaluating a CADe system in live endoscopic procedures [15].

Fockens et al. [16] studied the development and validation of CADe system training using a dataset of 14046 images from 15 centers. It presented results of sensitivity and specificity for all-comers image test set with 90% and 70% and all-comers video test set with 90% and 70%.

Hussein et al. [17] developed CNN-based systems using highdefinition-WLE and 119 i-scan-based videos, including dysplastic and non-dysplastic BE (NDBE). It presented a per-image sensitivity in the test set of 91%, specificity of 79%, and area under the receiver operator curve of 93% to detect dysplasia [17].

More recently, Takeda et al. [18] applied a Vision Transformer (Vit-B/16-384) to diagnose short-segment BE (SSBE) with WLE and linked color imaging (LCI). It has a similar specialist accuracy, with results of 84.1% for white-light imaging (WLI) and 90.5 for LCI.

Similar to the application of the CNN model for the classification of pathological images, machine learning, including supervised learning, is essential for pathologists in the diagnostic process. CNN-based architectures such as ResNet, Inception, and custom deep feature extractors are commonly used and often combined with weakly supervised or unsupervised learning strategies to reduce the annotation burden. In this sequence, a large amount of endoscopic data is processed to develop diagnostic models that are independent of labeling. Table 2 presents a summary of outcomes from current studies of BE histopathologic diagnosis using deep learning applications.

For the histological diagnostic confirmation of BE and associated neoplasia, deep learning is highly applicable, as supported by current studies.

Tomita et al. [19] evaluated a novel deep learning model that utilizes tissue-level annotations with high resolution histological image analysis for detection of BE and EAC. The proposed model presented classification accuracy with 0.85 (95% confidence interval [CI], 0.81–0.90) for the BE-no-dysplasia class, 0.89 (95% CI, 0.84–0.92) for the BE-with-dysplasia class, and 0.88 (95% CI, 0.84–0.92) for the adenocarcinoma class [19]. It also presented a mean accuracy of 0.83 (95% CI, 0.80–0.86) and marginal outperformance compared with the sliding window approach on the same testing set of esophagus tissue with 4 classes [19].

Faghani et al. [20] developed a deep learning model to predict dysplasia grades using whole-slide imaging. There were 542 patients’ slides with NDBE, low-grade dysplasia (LGD), and high-grade dysplasia (HGD) included in the study [20]. LGD has 81.3% sensitivity and 100% specificity, and NDBE as well as HGD reached >90% [20]. The overall positive predictive value and sensitivity metric was 0.91 for NDBE, 0.90 for LGD, and 1.0 for HGD [20]. It demonstrated potential for improving diagnosis of BE dysplasia as well as endoscopic treatment application [20].

Although the promising and beneficial expectations of the current studies outcomes provide positive feedback, there are several crucial limitations that should be considered in future studies.

First, there is a lack of large-scale multicenter randomized trials in the field of published AI EC. Most current studies are retrospective or single-center investigations, and image datasets are more common. Future follow-up study scales and methodologies must be considered to improve the reliability of the data applications and clinical outcomes.

Second, the general application of AI systems in the current environments of specific, selected, and distinct study fields is limited. Selected endoscope models are applied, and highly expert optimized centers are involved in training, raising questions on providing standard outcomes for the general population in the real world.

Third, controlling the variability of the label definition and ground-truth assignment is another issue. Since the estimated diagnostic accuracy is affected by the annotation strategy (still images versus videos), reference standards (biopsy versus resection pathology), and the use of consensus labeling, it is important to control for heterogeneity across studies.

Finally, the potential unintended consequences of deskilling behaviors of endoscopists affected by AI CAD systems is concerning. Although results from upper gastrointestinal endoscopy are limited, evidence from AI applications in colonoscopy raises concerns about prolonged reliance on CAD systems, which may alter endoscopist behavior resulting in reduced diagnostic ability when using non-AI detection methods.

Considering the current limitations of AI applications in the real world, the introduction of quality-control-based AI systems may contribute to standardizing examination quality and reducing operator-dependent variability. Quality-controlled AI systems focus on the adequacy of liminal distension, complete mucosal visualization with minimal blind spots, ensuring systematic inspection, and appropriate inspection time. Therefore, quality-controlled AI systems offer immediate feedback to reduce missed esophageal neoplasia in routine clinical practice. Notably, current commercially available systems provide detection assistance (CADe) and procedure monitoring, rather than diagnosis (CADx).

The ENDOANGEL system is a commercially available AI system approved by the National Medical Products Administration (NMPA) of China that provides quality control monitoring and detection assistance with blind-spot detection, inspection time monitoring, and real-time alerts. In a multicenter study, the ENDOANGEL system demonstrated reductions in blind spots (mean 5.38 [standard deviation (SD) 4.32] vs. 9.82 [SD 4.98]; p<0.001) and longer inspection times (5.40 [SD 3.82] min vs. 4.38 [SD 3.91] min; p<0.001) demonstrating the potential improvement in quality control [26].

The ENDO-AID system is another commercially available CAD system that integrates the Olympus EVIS X1 endoscopic system. It has enhanced visualization and detection assistance with CADe, CADx and AI-assisted imaging functions [27]. A recent retrospective study including 2000 patients presented significant improvement of mean polyp per patient score in the AI group (1.26) compared with the control group (0.85) (p<0.001) [27].

The WISENSE system is a real-time quality-controlled platform demonstrating a reduced blind spot rate in EGD procedures in comparison to the control group (5.86% vs. 22.46%, p<0.001) [28].

Therefore, future research focusing on the clinical endpoints of missing rates, interval cancer rates, and evaluation of unnecessary biopsies or procedures would advance the field of study of comparing AI-augmented quality-controlled, lesion-detection CADe, and a combination of quality-control with CADe to overcome the limitations of real-world AI applications.

AI has rapidly advanced as a supportive tool for the detection and characterization of esophageal precancerous lesions and EC with the development of various AI architectures, including ResNet-based CNNs, YOLO-based real-time detectors, U-Net segmentation models, and ViTs. Current evidence demonstrates that AI models improve the detection of Barrett’s neoplasia and early ESCC, assist in margin delineation, and support invasion-depth assessment using microvascular pattern analysis. However, there are limitations that require the development of quality-control-focused AI systems that standardize endoscopic examinations and reduce blind spots for the practical clinical endpoints of achieving advancement in missing rates, interval cancer incidence, and endoscopist performance.