Proton pump inhibitor (PPI)-based triple therapy consisting of PPI, amoxicillin, and clarithromycin, is the recommended first-line treatment for Helicobacter pylori infection. However, the eradication rate of triple therapy has declined over the past few decades. We analyzed the eradication rate and adverse events of triple therapy to evaluate current practices in Korea. A comprehensive literature search was performed up to August 2013 of 104 relevant studies comprising 42,124 patients. The overall eradication rate was 74.6% (95% confidence interval [CI], 72.1%-77.2%) by intention-to-treat analysis and 82.0% (95% CI, 80.8%-83.2%) by per-protocol analysis. The eradication rate decreased significantly from 1998 to 2013 (P < 0.001 for both intention-to-treat and per-protocol analyses). Adverse events were reported in 41 studies with 8,018 subjects with an overall incidence rate of 20.4% (95% CI, 19.6%-21.3%). The available data suggest that the effectiveness of standard triple therapy for H. pylori eradication has decreased to an unacceptable level. A novel therapeutic strategy is warranted to improve the effectiveness of first-line treatment for H. pylori infection in Korea.

Hasanuzzaman M, et al. J Korean Med Sci. 2024 Jan;39(4):e44. https://doi.org/10.3346/jkms.2024.39.e44

Background: The evaluation of large language models (LLMs) in medicine has undergone a shift from knowledge-based testing to practice-based assessment, representing an evolution in how we measure artificial intelligence readiness for clinical deployment. While LLMs now routinely exceed human performance on medical licensing examinations, their translation to clinical practice remains poorly characterized. Objective: This systematic review aims to categorize and analyze medical LLM benchmarks, examining performance patterns across different evaluation paradigms and identifying gaps in current assessment methodologies. Methods: The protocol was registered at PROSPERO (CRD420251139729). Four databases (MEDLINE/PubMed, Embase/Ovid, Cochrane Library, and arXiv) were searched from inception to August 31, 2025, using keywords related to clinical medicine benchmarks in LLMs. Studies were included if they (1) investigated clinical medicine benchmarks in LLMs, (2) were published in English, and (3) were available in full-text. Studies were excluded if they evaluated nonmedical domains or lacked benchmark validation. Methodological quality was assessed using the Mixed Methods Appraisal Tool (version 2018) by 2 independent reviewers (κ=0.91). Due to heterogeneity in evaluation metrics preventing meta-analysis, narrative synthesis was conducted using structured categorization of benchmark types. Results: From 3917 screened records, 39 medical LLM benchmarks were identified and categorized into 21 (54%) knowledge-based, 15 (38%) practice-based, and 3 (8%) hybrid frameworks. These benchmarks collectively encompass over 2.3 million questions across 45 languages and 172 medical specialties. Traditional knowledge-based benchmarks show saturation with leading models achieving 84%-90% accuracy on USMLE (United States Medical Licensing Examination)–style examinations, approaching or exceeding average physician performance. However, practice-based assessments reveal performance challenges, with specific benchmarks showing varied results: DiagnosisArena 45.82% (95% CI 42.9%-48.8%), MedAgentBench 69.67% (95% CI 64.2%-74.6%), and HealthBench 60% (95% CI 58.6%-61.3%) success rates, with practice-based benchmarks showing lower performance (45%-69%) compared to knowledge benchmarks (84%-90%). Task-specific analysis revealed differential performance patterns: factual retrieval maintained 85%-93% accuracy, clinical reasoning dropped to 50%-60%, diagnostic tasks achieved 45%-55% success, and safety assessment showed significant gaps at 40%-50% accuracy despite being life-critical. Geographic representation spans 6 continents with 18 (46%) benchmarks, incorporating non-English content. Quality assessment revealed 26% (10/39) of benchmarks had insufficient methodological reporting for complete evaluation. Conclusions: This systematic review provides the first comprehensive analysis quantifying the significant “knowledge-practice gap” in medical artificial intelligence: high performance on knowledge-based examinations (84%-90%) does not translate to clinical competence (45%-69%), with safety assessments at 40%-50%. Our findings provide quantitative evidence for regulators and health systems that examination scores are insufficient and misleading proxies for clinical readiness. This review concludes that autonomous deployment is not currently justifiable and that all evidence-based implementation strategies must mandate practice-oriented validation and robust human-in-the-loop oversight to ensure patient safety. Trial Registration: PROSPERO CRD420251139729; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251139729

Background: Ingested foreign objects frequently require emergency removal. This study aimed to investigate the clinical outcomes of endoscopic removal of foreign bodies from the upper gastrointestinal tract and the risk factors for adverse events. Methods: Adults (> 18 years) who underwent endoscopic management of ingested foreign bodies at two centers, one inland and one on the coast, between January 2008 and December 2017 were eligible. Clinical characteristics and procedure-related outcomes were retrospectively reviewed. Patients were divided into two groups, based on whether the foreign bodies were sharp or blunt in shape. Results: A total of 853 patients aged 19–96 years were analyzed. Ingestion of fish bones was more common in the coastal area, whereas ingestion of food boluses was more common in the inland area. The duration of impaction ranged from 1 h to over 1 month and was significantly longer in patients who ingested blunt than sharp foreign bodies (15 vs. 5 h, p < 0.001). Most (98.9%) foreign bodies were successfully removed. Adverse events occurred in 31.2 % of patients, including ulcers (4.0%) and perforations (3.3 %). Multivariate analysis showed that age (odds ratio [OR] 1.015, p = 0.012), sharp foreign bodies (OR 5.133, p < 0.001), location in the esophagus (OR 2.723, p = 0.018), and duration of impaction (OR 1.431, p < 0.001) were factors associated with adverse events. Conclusions: Early recognition and timely endoscopic removal of ingested foreign bodies, particularly in elderly patients and those with sharp foreign bodies, may improve clinical outcomes.

Background: As health care continues to evolve with technological advancements, the integration of artificial intelligence into clinical practices has shown promising potential to enhance patient care and operational efficiency. Among the forefront of these innovations are large language models (LLMs), a subset of artificial intelligence designed to understand, generate, and interact with human language at an unprecedented scale. Objective: This systematic review describes the role of LLMs in improving diagnostic accuracy, automating documentation, and advancing specialist education and patient engagement within the field of gastroenterology and gastrointestinal endoscopy. Methods: Core databases including MEDLINE through PubMed, Embase, and Cochrane Central registry were searched using keywords related to LLMs (from inception to April 2024). Studies were included if they satisfied the following criteria: (1) any type of studies that investigated the potential role of LLMs in the field of gastrointestinal endoscopy or gastroenterology, (2) studies published in English, and (3) studies in full-text format. The exclusion criteria were as follows: (1) studies that did not report the potential role of LLMs in the field of gastrointestinal endoscopy or gastroenterology, (2) case reports and review papers, (3) ineligible research objects (eg, animals or basic research), and (4) insufficient data regarding the potential role of LLMs. Risk of Bias in Non-Randomized Studies—of Interventions was used to evaluate the quality of the identified studies. Results: Overall, 21 studies on the potential role of LLMs in gastrointestinal disorders were included in the systematic review, and narrative synthesis was done because of heterogeneity in the specified aims and methodology in each included study. The overall risk of bias was low in 5 studies and moderate in 16 studies. The ability of LLMs to spread general medical information, offer advice for consultations, generate procedure reports automatically, or draw conclusions about the presumptive diagnosis of complex medical illnesses was demonstrated by the systematic review. Despite promising benefits, such as increased efficiency and improved patient outcomes, challenges related to data privacy, accuracy, and interdisciplinary collaboration remain. Conclusions: We highlight the importance of navigating these challenges to fully leverage LLMs in transforming gastrointestinal endoscopy practices. Trial Registration: PROSPERO 581772; https://www.crd.york.ac.uk/prospero/