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# Bibliometric Analysis

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  1. Frontiers in Oncology

    Bibliometric analysis of 608 AI breast cancer imaging studies finds diagnosis at 71.22% and explainable AI with a 1.00 burst ratio as the hottest frontier

    Using Scopus and Web of Science and a PRISMA workflow that narrowed 4,831 records to 608 peer-reviewed journal articles and reviews from 2020 to 2026, this study applied Bibliometrix and VOSviewer for a task-aware, methodology-centric bibliometric and thematic analysis, finding that diagnosis accounts for 71.22% of studies, mammography for 42.11%, explainable AI shows the strongest burst ratio at 1.00, while treatment-response prediction covers only 2.30% and only about 15-25% of studies explicitly report hyperparameter tuning strategies.

    University of LouisvilleAssiut UniversityMansoura UniversityTaibah UniversityMidway University
    Yathreb Bayan Mohamed（First author）Mohamed Shehata（Corresponding author）Mostafa A. Elhosseini（Corresponding author）
    Bibliometric AnalysisSystematic ReviewAI-based Breast Cancer DetectionExplainable AIHyperparameter Optimization
  2. medRxiv

    AI-Based Synthetic Data in Biomedicine: A Decade of Growth and a Persistent Translation Gap

    This study conducted a systematic mapping and bibliometric analysis of 4,143 publications from 2015 to 2025 on AI-generated synthetic data in biomedicine, combining expert annotation with LLM-assisted classification across data modality, medical domain, paper type, deployment status, and research stance, finding continuous growth in publication volume, 77.8% of papers strongly supportive with critical work below 1%, medical imaging dominating the corpus, highly cited primary research concentrated in molecular and pharmaceutical applications, and only 27 publications reporting operational use, thereby revealing a gap between methodological growth and deployment.

    Hadassah Medical CenterEindhoven University of TechnologyCARTIFHebrew University of JerusalemBrown UniversityIBM (United States)
    Navid Asgari（First author）Michal Rosen‐Zvi（Corresponding author）
    AI-Generated Synthetic Data in BiomedicineBibliometric AnalysisLLM-Assisted Classification

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