Stroke poses a significant health burden among hypertensive patients, where traditional risk models often lack precision. Machine learning (ML) has shown promise in enhancing prediction accuracy by integrating diverse data sources. Following PRISMA guidelines, we searched 5 databases from inception to September 2025. Eligible studies reported the performance of ML models in hypertensive cohorts. Data were pooled using random-effects models, with heterogeneity assessed via I², subgroup analyses, meta-regression, and leave-one-out sensitivity. The risk of bias was evaluated using PROBAST + AI, and the evidence quality was assessed using the GRADE approach. Ten studies (n = 13,299 stroke cases) were included. Pooled sensitivity was 0.88 (95 % CI: 0.80–0.93), specificity 0.88 (95 % CI: 0.77–0.94), positive likelihood ratio 7.1 (95 % CI: 3.4–15.1), negative likelihood ratio 0.14 (95 % CI: 0.08–0.26), and AUC-ROC 0.94 (95 % CI: 0.91–0.96), indicating good discriminative ability. Heterogeneity was high for both sensitivity (I² = 79.5 %) and specificity (I² = 76.8 %), potentially due to variations in study design and populations. Subgroup analyses showed consistent performance in Chinese studies (sensitivity 0.85, specificity 0.84) and those using multimodal features (sensitivity 0.84, specificity 0.83), with higher sensitivity for ischemic/hemorrhagic-specific models (0.90). Meta-regression explained 73.9 % of variance and No publication bias was detected (Deeks' p = 0.654). ML models demonstrate good performance for stroke prediction in hypertensive patients. However, heterogeneity underscores the need for standardized approaches. This evidence, rated moderate by GRADE, supports ML integration in clinical practice for improved prevention.

Artificial intelligence (AI) networks offer significant potential for predicting immunotherapy outcomes in gastrointestinal cancers by analyzing genetic mutation profiles. Their application in prognosis remains underexplored. This systematic review and meta-analysis aim to evaluate the effectiveness of AI-based models, which refers to systems utilizing artificial intelligence to analyze data and make predictions, in predicting immunotherapy responses in gastrointestinal cancers using genetic mutation features. This study, adhering to PRISMA guidelines, aimed to evaluate AI networks for predicting gastrointestinal cancer prognosis in response to immunotherapy using genetic mutation features. A search in PubMed, WOS, and Scopus identified relevant studies. Data extraction and quality assessment were conducted, and statistical analysis included pooled estimates for sensitivity, specificity, accuracy, and AUC. Regression models and imputation methods addressed missing values, ensuring accurate and robust results. STATA version 18 was used to analyze the data. A total of 45 studies, all published in 2024, involving 14,047 participants in training sets and 10,885 participants in test sets, were included. The pooled results of AI model performance for gastrointestinal cancers based on genetic mutation features were: AUC = 0.86 (95% CI: 0.86–0.87), Sensitivity = 83% (95% CI: 83%-84%), Specificity = 72% (95% CI: 72%-73%), and Accuracy = 82% (95% CI: 82%-83%). Heterogeneity was low to moderate, and no publication bias was detected. Subgroup analysis showed higher AUC for gastric cancer models (AUC: 0.87) and lower for pancreatic cancer models (AUC: 0.52). AI networks demonstrate promising potential in predicting immunotherapy outcomes for gastrointestinal cancers based on genetic mutation features. This systematic review highlights their effectiveness in stratifying patients and optimizing treatment decisions. However, further large-scale studies are needed to validate AI models and integrate them into clinical practice for improved precision in cancer immunotherapy.

Background: Heart failure is a significant complication in diabetic patients, and machine learning algorithms offer potential for early prediction. This systematic review and meta‐analysis evaluated the diagnostic performance of ML models in predicting HF among diabetic patients. Methods: We searched PubMed, Web of Science, Embase, ProQuest, and Scopus, identifying 2830 articles. After deduplication and screening, 16 studies were included, with 7 providing data for meta‐analysis. Study quality was assessed using PROBAST+AI. A bivariate random‐effects model (Stata, midas, metadta) pooled sensitivity, specificity, likelihood ratios, and diagnostic odds ratio (DOR) for best‐performing algorithms, with subgroup analyses. Heterogeneity ( I ² ) and publication bias were assessed. Results: This meta‐analysis of seven studies evaluating machine learning models for heart failure detection demonstrated a pooled sensitivity of 84% (95% CI: 0.75–0.90), specificity of 86% (95% CI: 0.56–0.97), and an area under the ROC curve of 0.90 (95% CI: 0.87–0.93). The pooled positive likelihood ratio was 6.6 (95% CI: 1.2–35.9), and the negative likelihood ratio was 0.17 (95% CI: 0.08–0.36), with a diagnostic odds ratio of 39 (95% CI: 4–423). Significant heterogeneity was observed, primarily related to differences in study populations, machine learning algorithms, dataset sizes, and validation methods. No significant publication bias was detected. Conclusion: Machine learning models demonstrate promising diagnostic accuracy for heart failure detection and have the potential to support early diagnosis and risk assessment in clinical practice. However, considerable heterogeneity across studies and limited external validation highlight the need for standardised development, prospective validation, and improved interpretability of ML models to ensure their effective integration into healthcare systems.