Opportunities and challenges of artificial intelligence-assisted endoscopy and high-quality data for esophageal squamous cell carcinoma

Table 3 Summary of reviews and meta-analysis on esophageal squamous cell carcinoma using artificial intelligence

Ref.	Year	Primary outcomes	Secondary outcomes	Patients studied	Type of data	Study design
Zhang et al[63]	2020	Not applicable	Not applicable	Not applicable	Not applicable	Mini-review
Syed et al[82]	2020	Summary of literature using DL to identify esophageal tumors	DL/CNN overview, current limitations, and future directions	Not applicable (not focused on individual study data; 21 relevant studies reviewed)	Endoscopic images with histology-confirmed clinical data	Mentored review
Huang et al[83]	2020	Not applicable	Not applicable	Not applicable	Not applicable	Mini-review
Lazăr et al[84]	2020	ML model comparison for endoscopic esophageal lesion assessment	AI in care quality, workflow efficiency, and physician support	Not applicable (review of multiple studies)	Review of AI in endoscopic assessment of esophageal disease	Review
Namikawa et al[85]	2020	Review of studies using CNNs in gastrointestinal endoscopy	AI in GI polyp/cancer detection and capsule endoscopy	Not applicable	Not applicable	Review article
Zhang et al[1]	2021	Diagnostic accuracy of AI-assisted models	AI vs endoscopists: Pooled accuracy and subgroup performance	Not applicable	Endoscopic media and histology-confirmed clinical data	Meta-analysis
Liu et al[44]	2021	Not applicable	Not applicable	Not applicable	Not applicable	Mini-review
Ma et al[13]	2022	CNN-AI for early EC detection from endoscopy	Heterogeneity and bias analysis (I2, meta-regression, Deeks’ plot)	Meta-analysis of 7 studies; number of patients and images varied across studies	Meta-analysis of WLE/NBI-based studies with histological confirmation	Meta-analysis
Nagao et al[64]	2022	AI in upper GI: Current status, clinical integration, and future outlook	AI in H. pylori infection diagnosis, gastric anatomy, and upper GI cancer detection	Not applicable (review of multiple studies)	CNN-based AI for diagnosis of GI and pharyngeal cancers	Review
Tokat et al[86]	2022	Overview of the applicability of AI technology in upper gastrointestinal endoscopy	Clinical AI challenges and applications in GI oncology	Not applicable	WLE/NBI/ME-NBI images with clinical and histological data	Review article
Guidozzi et al[14]	2023	AI in endoscopic diagnosis of esophageal cancer	AI vs endoscopists, tumor types, and bias risk (QUADAS-2)	1590 patients for ESCC diagnosis (14 studies); 478 patients for EAC diagnosis (9 studies)	English studies using endoscopic images/videos	Systematic review and meta-analysis
Pan et al[87]	2023	AI in early ESCC endoscopic diagnosis: Current status	Discussion of limitations and future prospects of AI in ESCC diagnosis	Not applicable (review of multiple studies)	Review of DL models for ESCC diagnosis	Review
Tao et al[32]	2024	AI vs experts: Accuracy in early EC and depth diagnosis	AI vs endoscopists: Performance, invasion depth diagnosis, and bias assessment	Not applicable	Meta-analysis of 19 studies with heterogeneous sample sizes	Meta-analysis
Kikuchi et al[88]	2024	Recent studies on endoscopic AI for GI tumors	Discussion on the future outlook of AI in gastroenterology	Not applicable	Published studies on DL-based endoscopic AI	Review
Yan et al[33]	2025	Not applicable	Not applicable	Not applicable	Not applicable	Review article
Shahzil et al[89]	2025	Lesion color contrast, visibility, and GI detection rate	Lesion visibility and analysis of serrated lesions and adenomas	16634 individuals (data integrated from 17 studies)	Clinical and endoscopic image data	Systematic review and meta-analysis

AI: Artificial intelligence; CNN: Convolutional neural network; DL: Deep learning; EAC: Esophageal adenocarcinoma; EC: Esophageal cancer; ESCC: Esophageal squamous cell carcinoma; GI: Gastrointestinal; H. pylori: Helicobacter pylori; ME: Magnifying endoscopy; ML: Machine learning; NBI: Narrow-band imaging; WLE: White light endoscopy.