Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 6 appraisals
BMJ open
Endoscopic biopsy strategies for gastric ulcers: an updated systematic review
OBJECTIVE: To systematically summarise recent evidence on endoscopic biopsy for gastric ulcer. DESIGN: Systematic review of clinical studies. DATA SOURCES: PubMed, Embase, Cochrane Library, Web of Science, CNKI and CiNii Research were searched for studies published from 2015 to May 2025. ELIGIBILITY CRITERIA: Original studies involving adults with endoscopically diagnosed gastric ulcers or ulcerative gastric lesions directly informing biopsy strategy were eligible if they evaluated biopsy timing, number, site or related diagnostic outcomes. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently screened studies, extracted data and assessed risk of bias using the Newcastle-Ottawa Scale. Owing to substantial clinical and methodological heterogeneity, findings were synthesised narratively. RESULTS: Seven studies were included and grouped into four domains: biopsy timing, biopsy number, biopsy site and safety. Available data suggested that most malignancies may be identifiable at the initial endoscopy examination although repeat biopsy during ulcer healing or scarring might still detect some missed cancers. Evidence on the biopsy site remained limited and inconsistent, and findings from healed-phase ulcers further suggested that the anatomical site alone may be insufficient to guide biopsy strategy. Available studies also indicated that four well-targeted biopsies may be adequate for some depressed or polypoid lesions whereas more extensive sampling might be needed for infiltrative or otherwise suspicious lesions. Limited safety data did not show a clear increase in adverse events with broader biopsy strategies or the biopsy of stabilised bleeding ulcers. CONCLUSIONS: Contemporary evidence on endoscopic biopsy for gastric ulcers remains limited, heterogeneous and largely observational. Available data suggest that biopsy at initial endoscopy and adequate, lesion-targeted sampling may improve diagnostic yield, while follow-up and intensive sampling should be individualised according to endoscopic appearance, histological findings and clinical suspicion. PROSPERO REGISTRATION NUMBER: CRD420251157851.
21 July 2026
Read appraisal →International journal of medical informatics
Artificial intelligence in gastroenterology clinical practice: Scoping review of large language model applications.
BACKGROUND: Advances in artificial intelligence have brought renewed attention to tools that can work with the large amount of written information generated in clinical practice. Among these, large language models (LLMs) stand out for their ability to interpret and generate medical text in a flexible, context-aware way. Gastroenterology, like many specialties, produces a wide range of narrative and semi-structured data, and this has encouraged researchers to explore how LLMs might help clinicians manage everyday tasks. Recent studies have examined their potential contributions to patient education, communication between care teams and patients, decision support, and routine documentation, reflecting a growing interest in how these systems might fit into real-world clinical workflows. OBJECTIVE: This scoping review aimed to map current applications of LLMs in gastroenterology clinical practice, including subspecialty focus, study designs, model types, and reported outcomes. METHOD: Following PRISMA guidelines, a systematic search was conducted in PubMed, Scopus, and Web of Science for studies published between January 2022 and August 2025. Eligible studies included original research assessing LLM applications in gastroenterology clinical practice. Data were extracted on subspecialty, application domain, LLM type, data source, and outcomes. We employed thematic analysis to address our primary research question. RESULTS: 73 out of 2895 studies identified in the initial search met the inclusion criteria. Six subspecialties and six application domains emerged from our review. Hepatology (20/73 studies, 27.3%) and endoscopy (17/73 studies, 23.2%) were the most represented subspecialties. The most frequently investigated application domains were patient education and communication (38 studies) and decision support and clinical guidance (24 studies). Most studies were simulation-based or literature-based cases, although an increasing number have used real-world clinical data, particularly in recent years. The majority evaluated general-purpose models such as GPT-3.5 and GPT-4, with some incorporating retrieval augmentation or fine-tuning. Reported outcomes varied by application domain and included measures of accuracy, concordance, completeness, relevance, safety, reliability, usability, user satisfaction, efficiency, time savings, and educational value. Commonly described limitations included variable reliability, incomplete responses, and challenges in generalizing from simulated to clinical settings. CONCLUSIONS: Research on LLMs in gastroenterology has expanded across multiple subspecialties and application domains. Current evidence is primarily based on simulation studies, with limited but growing evaluation using real-world clinical data. Further work is needed to assess performance in prospective and applied clinical contexts.
2 July 2026
Read appraisal →Journal of gastrointestinal and liver diseases : JGLD
Colon Cleaning after Inadequate Bowel Preparation: A Pooled-data Analysis
BACKGROUND AND AIMS: An adequate bowel preparation is essential for performing high-quality ccolonoscopy; however, it remains ineffective for a certain percentage of patients. We conducted a literature search to identify evidence on salvage strategies following inadequate bowel preparation. METHODS: The search of medical literature was conducted using Medline and Google Scholar, restricted to studies published in English as a full paper. From retrieved studies, the success rate was calculated according to the rescue approaches used, including different oral- or device-assisted procedures, and pooling data of similar approaches. RESULTS: By considering data of 17 studies with 2,167 patients, an adequate colon cleaning was overall achieved in 79.3% (95%CI: 77.5-80.1). By grouping data of similar approaches, the success rate was 78.1% with oral high-volume polyethylene glycol (PEG), 75.3% with low-volume PEG, 82.5% with oral sennosides, and 84.6% following colonoscope or device-assisted preparation. CONCLUSIONS: Effective bowel cleansing is not achieved in nearly 20% of patients due to inadequate bowel preparation and although multiple approaches have been investigated, none has consistently provided adequate bowel preparation for all patients.
29 June 2026
Read appraisal →Journal of bronchology & interventional pulmonology
AIBAN-AI Bronchial Detection and Airway Navigation System
BACKGROUND: Accurate navigation within the lungs is critical for bronchoscopy procedures but remains challenging due to the complex airway anatomy. Artificial intelligence (AI) integrating real-time landmark detection and navigation guidance may enhance precision and safety. METHODS: We developed an AI Bronchial detection and Airway Navigation system (AIBAN) for bronchoscopy, combining airway lumen detection, anatomic localization, and navigation guidance. A YOLOv8-based detection module was used to identify 34 airway landmarks in real time. Next, an airway-level localization module estimated the bronchoscope's position using a hierarchical anatomic graph, identifying the deepest branch compatible with all coherent detections in each frame. A navigation module then recommended the next airway to follow along predefined anatomic paths, providing directional cues to perform a complete and systematic bronchoscopy. RESULTS: AIBAN was developed on a data set of 18 bronchoscopy videos, using 15 for training and 3 for validation. In validation, the framework correctly identified the bronchoscope's location in 91.1% of the frames. In addition, 92.6% of predictions were within one airway branch of the true location, and 94.5% were within 2 branches. The navigation guidance performance was also promising, with the system following the correct anatomic pathway in 96.2% of test videos, demonstrating reliable guidance through the lungs. CONCLUSION: AIBAN enables accurate airway detection, localization, and navigation guidance in a simulation-based setting. It can provide reliable guidance along predefined paths and precise spatial positioning and is a promising tool for medical training and has potential for future clinical implementation.
11 May 2026
Read appraisal →Abdominal radiology (New York)
Artificial intelligence in gastric cancer: a systematic review of machine learning and deep learning applications
BACKGROUND: Gastric cancer (GC) remains a major global health concern, ranking as the fifth most prevalent malignancy and the fourth leading cause of cancer-related mortality worldwide. Although early detection can increase the 5-year survival rate of early gastric cancer (EGC) to over 90%, more than 80% of cases are diagnosed at advanced stages due to subtle clinical symptoms and diagnostic challenges. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has shown great promise in addressing these limitations. OBJECTIVES: This systematic review aims to evaluate the performance, applications, and limitations of ML and DL models in GC management, with a focus on their use in detection, diagnosis, treatment planning, and prognosis prediction across diverse clinical imaging and data modalities. METHODS: Following the PRISMA 2020 guidelines, a comprehensive literature search was conducted in MEDLINE, Web of Science, and Scopus for studies published between 2004 and May 2025. Eligible studies applied ML or DL algorithms for diagnostic or prognostic tasks in GC using data from endoscopy, computed tomography (CT), pathology, or multi-modal sources. Two reviewers independently performed study selection, data extraction, and risk of bias assessment. RESULTS: A total of 59 studies met the inclusion criteria. DL models, particularly convolutional neural networks (CNNs), demonstrated strong performance in EGC detection, with reported sensitivities up to 95.3% and Area Under the Curve (AUCs) as high as 0.981, often exceeding expert endoscopists. CT-based radiomics and DL models achieved AUCs ranging from 0.825 to 0.972 for tumor staging and metastasis prediction. Pathology-based models reported accuracies up to 100% for EGC detection and AUCs up to 0.92 for predicting treatment response. Cross-modality approaches combining radiomics and pathomics achieved AUCs up to 0.951. Key challenges included algorithmic bias, limited dataset diversity, interpretability issues, and barriers to clinical integration. CONCLUSION: ML and DL models have demonstrated substantial potential to improve early detection, diagnostic accuracy, and individualized treatment in GC. To advance clinical adoption, future research should prioritize the development of large, diverse datasets, implement explainable AI frameworks, and conduct prospective clinical trials. These efforts will be essential for integrating AI into precision oncology and addressing the increasing global burden of gastric cancer.
4 Apr 2026
Read appraisal →Ann Med
Artificial intelligence-assisted diagnosis of early gastric cancer: present practice and future prospects
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.
1 Dec 2025
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