Artificial intelligence (AI) using deep-learning (DL) has emerged as a breakthrough computer technology. By the era of big data, the accumulation of an enormous number of digital images and medical records drove the need for the utilization of AI to efficiently deal with these data, which have become fundamental resources for a machine to learn by itself. Among several DL models, the convolutional neural network showed outstanding performance in image analysis. In the field of gastroenterology, physicians handle large amounts of clinical data and various kinds of image devices such as endoscopy and ultrasound. AI has been applied in gastroenterology in terms of diagnosis, prognosis, and image analysis. However, potential inherent selection bias cannot be excluded in the form of retrospective study. Because overfitting and spectrum bias (class imbalance) have the possibility of overestimating the accuracy, external validation using unused datasets for model development, collected in a way that minimizes the spectrum bias, is mandatory. For robust verification, prospective studies with adequate inclusion/exclusion criteria, which represent the target populations, are needed. DL has its own lack of interpretability. Because interpretability is important in that it can provide safety measures, help to detect bias, and create social acceptance, further investigations should be performed.

Background: Helicobacter pylori plays a central role in the development of gastric cancer, and prediction of H pylori infection by visual inspection of the gastric mucosa is an important function of endoscopy. However, there are currently no established methods of optical diagnosis of H pylori infection using endoscopic images. Definitive diagnosis requires endoscopic biopsy. Artificial intelligence (AI) has been increasingly adopted in clinical practice, especially for image recognition and classification. Objective: This study aimed to evaluate the diagnostic test accuracy of AI for the prediction of H pylori infection using endoscopic images. Methods: Two independent evaluators searched core databases. The inclusion criteria included studies with endoscopic images of H pylori infection and with application of AI for the prediction of H pylori infection presenting diagnostic performance. Systematic review and diagnostic test accuracy meta-analysis were performed. Results: Ultimately, 8 studies were identified. Pooled sensitivity, specificity, diagnostic odds ratio, and area under the curve of AI for the prediction of H pylori infection were 0.87 (95% CI 0.72-0.94), 0.86 (95% CI 0.77-0.92), 40 (95% CI 15-112), and 0.92 (95% CI 0.90-0.94), respectively, in the 1719 patients (385 patients with H pylori infection vs 1334 controls). Meta-regression showed methodological quality and included the number of patients in each study for the purpose of heterogeneity. There was no evidence of publication bias. The accuracy of the AI algorithm reached 82% for discrimination between noninfected images and posteradication images. Conclusions: An AI algorithm is a reliable tool for endoscopic diagnosis of H pylori infection. The limitations of lacking external validation performance and being conducted only in Asia should be overcome. Trial Registration: PROSPERO CRD42020175957; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=175957

Korean Red Ginseng (KRG) is a well-known natural product with anticarcinogenic and antioxidant effects. We evaluated the antifatigue effect of KRG in patients with nonalcoholic fatty liver disease (NAFLD). Eighty patients with NAFLD were prospectively randomized to receive 3 wk of KRG or placebo in addition to counseling on healthy eating and regular exercise. Liver function test, proinflammatory cytokines, adiponectin, antioxidant activity, and fatigue score were measured and compared according to the body mass index between the KRG and placebo groups. The liver function tests were significantly improved after 3 wk of treatment in both groups. The mean levels (at baseline and after treatment) of tumor necrosis factor-α were 108.0 pg/mL ± 54.8 pg/mL and 92.7 pg/mL ± 39.0 pg/mL (p = 0.018) in the KRG group and 123.1 pg/mL ± 42.1 pg/mL and 127.5 pg/mL ± 62.2 pg/mL (p = 0.694) in the placebo group, respectively. There was a significant difference in change of adiponectin levels between the KRG (7,751.2 pg/mL ± 3,108.1 pg/mL and 8,197.3 pg/mL ± 2,714.5 pg/mL) and placebo groups (7,711.6 pg/mL ± 3,041.3 pg/mL and 7,286.1 pg/mL ± 5,188.7 pg/mL, p = 0.027). In patients with overweight, the fatigue score was significantly decreased in the KRG group (35.0 ± 13.2 and 24.5 ± 8.9, p = 0.019). Our results show that KRG might be effective in reducing proinflammatory cytokine and fatigue in overweight patients with NAFLD, in addition to improvements in adiponectin levels.

Background Deep learning models have previously been established to predict the histopathology and invasion depth of gastric lesions using endoscopic images. This study aimed to establish and validate a deep learning-based clinical decision support system (CDSS) for the automated detection and classification (diagnosis and invasion depth prediction) of gastric neoplasms in real-time endoscopy. Methods The same 5017 endoscopic images that were employed to establish previous models were used for the training data. The primary outcomes were: (i) the lesion detection rate for the detection model, and (ii) the lesion classification accuracy for the classification model. For performance validation of the lesion detection model, 2524 real-time procedures were tested in a randomized pilot study. Consecutive patients were allocated either to CDSS-assisted or conventional screening endoscopy. The lesion detection rate was compared between the groups. For performance validation of the lesion classification model, a prospective multicenter external test was conducted using 3976 novel images from five institutions. Results The lesion detection rate was 95.6 % (internal test). On performance validation, CDSS-assisted endoscopy showed a higher lesion detection rate than conventional screening endoscopy, although statistically not significant (2.0 % vs. 1.3 %; P = 0.21) (randomized study). The lesion classification rate was 89.7 % in the four-class classification (advanced gastric cancer, early gastric cancer, dysplasia, and non-neoplastic) and 89.2 % in the invasion depth prediction (mucosa confined or submucosa invaded; internal test). On performance validation, the CDSS reached 81.5 % accuracy in the four-class classification and 86.4 % accuracy in the binary classification (prospective multicenter external test). Conclusions The CDSS demonstrated its potential for real-life clinical application and high performance in terms of lesion detection and classification of detected lesions in the stomach.

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