ReviewWorld journal of gastroenterology2020
Artificial intelligence-assisted esophageal cancer management: Now and future.
Review in World journal of gastroenterology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 57 citations in OpenAlex.
- Use of volumetric analysis to predict pathologic response in esophageal cancer: a single-institution cohort study.Journal of thoracic disease · 2026Article
- Research progress of deep learning based on magnetic resonance imaging in meningioma.Magma (New York, N.Y.) · 2026Review
- A new paradigm for esophageal disease management: the application and future prospects of Shared Decision Making (SDM).World journal of surgical oncology · 2026Review
- Opportunities and challenges of artificial intelligence-assisted endoscopy and high-quality data for esophageal squamous cell carcinoma.World journal of gastrointestinal oncology · 2026Review
- L-Selenomethylselenocysteine Exerts Inhibitory Effects on the Progression of Esophageal Cancer by Targeting the PI3K/AKT Signaling Pathway.Recent patents on anti-cancer drug discovery · 2026Article
- Artificial intelligence-assisted diagnosis and histopathological grading of bladder cancer: current status, challenges, and future directions.Frontiers in digital health · 2026Review
- Nomographic predictive models for complications after minimally invasive esophagectomy: Current status and future perspectives.World journal of gastrointestinal surgery · 2025Review
- Future prospects of deep learning in esophageal cancer diagnosis and clinical decision support (Review).Oncology letters · 2025Review
- Personalized prediction of esophageal cancer risk based on virtually generated alcohol data.Journal of translational medicine · 2025Article
- Current Role of Artificial Intelligence in the Management of Esophageal Cancer.Journal of clinical medicine · 2025Review
- Gastrointestinal tract disease detection via deep learning based structural and statistical features optimized hexa-classification model.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Detection of Image Artifacts Using Improved Cascade Region-Based CNN for Quality Assessment of Endoscopic Images.Bioengineering (Basel, Switzerland) · 2023Article
- Third Eye? The Assistance of Artificial Intelligence (AI) in the Endoscopy of Gastrointestinal Neoplasms.Journal of clinical medicine · 2023Review
- Revolutionizing healthcare by use of artificial intelligence in esophageal carcinoma - a narrative review.Annals of medicine and surgery (2012) · 2023Review
- Enhancing the Evidence with Algorithms: How Artificial Intelligence Is Transforming Forensic Medicine.Diagnostics (Basel, Switzerland) · 2023Review
- Bio-inspired microfluidics: A review.Biomicrofluidics · 2023Review
- Artificial intelligence in esophageal cancer diagnosis and treatment: where are we now?-a narrative review.Annals of translational medicine · 2023Review
- Systematic meta-analysis of computer-aided detection to detect early esophageal cancer using hyperspectral imaging.Biomedical optics express · 2023Article
- AI-assisted identification of intrapapillary capillary loops in magnification endoscopy for diagnosing early-stage esophageal squamous cell carcinoma: a preliminary study.Medical & biological engineering & computing · 2023Article
- The Role of Artificial Intelligence in Gastric Cancer: Surgical and Therapeutic Perspectives: A Comprehensive Review.Journal of gastric cancer · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Esophageal cancer poses diagnostic, therapeutic and economic burdens in high-risk regions. Artificial intelligence (AI) has been developed for diagnosis and outcome prediction using various features, including clinicopathologic, radiologic, and genetic variables, which can achieve inspiring results. One of the most recent tasks of AI is to use state-of-the-art deep learning technique to detect both early esophageal squamous cell carcinoma and esophageal adenocarcinoma in Barrett's esophagus. In this review, we aim to provide a comprehensive overview of the ways in which AI may help physicians diagnose advanced cancer and make clinical decisions based on predicted outcomes, and combine the endoscopic images to detect precancerous lesions or early cancer. Pertinent studies conducted in recent two years have surged in numbers, with large datasets and external validation from multi-centers, and have partly achieved intriguing results of expert's performance of AI in real time. Improved pre-trained computer-aided diagnosis algorithms in the future studies with larger training and external validation datasets, aiming at real-time video processing, are imperative to produce a diagnostic efficacy similar to or even superior to experienced endoscopists. Meanwhile, supervised randomized controlled trials in real clinical practice are highly essential for a solid conclusion, which meets patient-centered satisfaction. Notably, ethical and legal issues regarding the black-box nature of computer algorithms should be addressed, for both clinicians and regulators.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.