Lung cancer remains a major global health burden, and deep learning, especially convolutional neural networks, has become a widely used approach for CT-based detection and diagnostic support. This scientometric study maps how this research area has developed and how its main themes and collaborations are organized, using a workflow that combines bibliometrix (R) with VOSviewer. From a broader Scopus query result set, an exported core subset of 200 journal articles published between 2017 and 2025 was selected for in-depth bibliometric analysis, spanning 139 sources and including 1,659 cited references. The citation overview reports 7,167 total citations across 175 cited documents, corresponding to a corpus h-index of 42. Thematic mapping and keyword co-occurrence analyses show a stable CT-centric backbone that connects most studies, alongside faster-growing streams focused on model generalization and optimization, and smaller niche lines such as 3D CNN–driven lung nodule detection. Collaboration indicators point to moderate internationalization, with 43 multi-country publications (21.5%). Overall, the field is consolidating around CT-based deep learning pipelines, while a key gap remains in translation-ready evidence, multi-center external validation, robustness to protocol heterogeneity, and more consistent reporting practices to support reproducibility and clinical adoption.

The combination of machine learning (ML) and neural networks (NN), specifically deep learning (DL), is making a big breakthrough to breast cancer studies. This scientometrics survey studies 200 highly cited publications to map the intellectual landscape and studies trends in this dynamic field. The survey discloses a dominant concentration on computer-aided diagnosis (CAD) systems using convolutional neural networks (CNNs) for the classification of breast cancer from different imaging modalities, including mammography, histopathology, ultrasound, and magnetic resonance imaging (MRI). Key survey directions identified include: (1) the development of comprehensive deep learning techniques for image-based detection and classification; (2) the application of transfer learning to resolve data scarcity; (3) the combination of multi-omics and clinical data for personalized prognosis and treatment prediction; and (4) the exploration of explainability and robustness in ML-driven clinical tools. This study synthesizes the methodological advancements, sheds light on the evolution from traditional machine learning to deep learning, and surveys the challenges associated with data heterogeneity, model interpretability, and clinical integration. By giving a structured overview of the seminal work and emerging paradigms, the study serves as a reference for graduate students and other interested parties to have a better understanding about the current state and future trajectories of AI in breast oncology.

The publication under consideration provides a detailed scientometric analysis of two hundred scientific publications, which were obtained from the Scopus database, and the main focus was the intersection of Magnetic Resonance Imaging (MRI), brain tumor research, and artificial intelligence (AI), or at least data science. The analytical process sharply defined the intellectual structure and development of the field from 2021 to 2025. Among the important observations was the predomination of the research interests directed at the deep learning models for segmentation and classification, the growing importance of Explainable AI (XAI) to mediate between the model's performance and clinical acceptance, and the flourishing of radiomics for oncological prediction. The geographical distribution of the field indicates that it is a world-wide activity with the major input coming from China, India, America, Germany, and Pakistan. The authors and institutions whose works have had the greatest impact are revealed, and they are frequently found being associated with the topics of taking the client in combination with data coming from different modalities through the process of data fusion and the clinical translation of technologies. This literature review maps out these trends in order to counsel the researchers who are just starting and in the meantime to indicate the directions of the future research, such as the need for solid and consistent; federated learning and creating lightweight models that are suitable for clinical deployment.