Every year, cardiovascular disease (CVD) claims the lives of nearly 17 million people worldwide. Predicting heart disease early and accurately can help delay therapies and improve results. Patient data analysis machine learning techniques have shown promise for better predictive capabilities than conventional methods; however, there are still gaps in areas such as algorithm blending, standardization, feature optimization, and model tuning that require strong methodology. By benchmarking against established methods, this study attempts to create a more sophisticated machine learning model with detailed performance and a robust approach for predicting heart disease. Using a clinical dataset that was obtained from an internet repository, an improved random forest (RF) model was created. It was then tested against baseline logistic regression and support vector machine models, Naïve Bayes Classifier, K Nearest Neighbors Classifier, and Decision Tree Classifier. RF hyperparameter tweaking, redundant feature filtering, and systematic data preprocessing were used. Accuracy, precision, recall, F1 score, and ROC analysis were computed as evaluation measures. With F1 score, 1.00 AUC, and 90% accuracy, The RF model demonstrated superior performance compared to the remaining models, which exhibited, AUCs of 0.9, 0.82, and 0.9. On the public dataset, the refined RF model demonstrated exceptional predictive performance, highlighting the promise of a methodical machine learning approach to improve heart disease prediction. The external clinical validation and optimization of various patient populations should be the main areas of attention for future research.

The demand for advanced detection methods and accurate staging remains a global challenge in cancer diagnosis. Even though traditional deep learning models in medical imaging achieve high precision, they suffer from limited explainability and multimodal reasoning due to their black-box nature, thereby limiting their clinical applicability. To address this gap, recent research has increasingly explored multimodal approaches that integrate visual and textual clinical data to enhance diagnostic accuracy and interpretability. This study presents a bibliometric analysis of 408 publications from 2021 to 2025, collected from Web of Science and Scopus, using VOSviewer and R-Bibliometrix to map citation networks, co-authorship, and keyword co-occurrences. The results reveal a rapid growth from 1 publication in 2021 to 269 in 2025, with significant contributions from leading countries and institutions. Thematic analysis indicates a shift from conventional convolutional approaches toward transformer-based and self-supervised methods, alongside increasing attention to multimodal learning in cancer imaging tasks such as breast, lung, and brain cancer analysis. Overall, this study provides a structured overview of the evolving research landscape, highlighting key trends, emerging themes, and research gaps to inform future developments in multimodal artificial intelligence for cancer diagnosis.

This study presents the first dedicated bibliometric analysis of artificial intelligence (AI) and deep learning applications in pediatric radiology and medical imaging, mapping the intellectual structure of a rapidly evolving field. A total of 2688 articles and conference proceedings published between 2005 and 2025 were retrieved from the Web of Science Core Collection and analyzed using Bibliometrix R and VOSviewer. The findings reveal exponential growth in publications, from 7 papers in 2005 to 559 in 2025, with journal articles dominating the corpus (85.9%). The most-cited contributions, led by Kermany et al. (2018) with 2886 citations, are predominantly technical feasibility studies rather than clinical outcome trials, indicating a field that has advanced methodologically but remains in early stages of clinical translation. Thematic mapping identifies convolutional neural networks, pneumonia, and transfer learning as Motor Themes representing methodological maturity in chest imaging, while neuroimaging and image segmentation clusters occupy Niche Themes, reflecting insular development with limited cross-field connectivity. Geographic analysis reveals concentrated co-authorship along US–China and US–Europe corridors, with African, Latin American, and Southeast Asian institutions largely absent from knowledge production networks. Eight of the ten most productive affiliations are North American, highlighting structural inequities that risk producing AI tools optimized for high-resource settings rather than the global pediatric population. This analysis provides an empirical foundation for reorienting the field toward clinical validation, geographic inclusion, and methodological integration across isolated research communities.