The landscape of machine learning in clinical applications: A thematic mapping of evolution, frontiers, and future opportunities

Background Machine learning (ML) has become a transformative force in clinical research, offering predictive precision and data-driven decision-making across diverse medical domains. Despite this rapid adoption, a comprehensive informatic-based synthesis of ML applications in clinical trials remains lacking. This study systematically maps the scientific landscape, thematic evolution, and emerging directions of ML-related clinical trial research. Methods The analysis was conducted on PubMed-indexed clinical trials (1995–2025) using Bibliometrix R package , VOSviewer , and Microsoft Excel 2021 (Microsoft Corp., USA). Temporal trends were modeled using ARIMA(5,1,0) forecasting and additive time-series decomposition. Collaboration networks, productivity patterns (Lotka’s Law), journal dispersion (Bradford’s Law), keyword co-occurrence, and thematic mapping (Walktrap clustering, Callon’s centrality/density) were analyzed to identify conceptual structures and research frontiers. Results A total of 1,195 publications across 563 journals were identified, showing exponential growth after 2018 and a forecasted stabilization by 2030. The USA (24.8%) and China (19.5%) led global output, reflecting strong North American–Asian collaboration. Keyword co-occurrence revealed eight clusters centered on machine learning , artificial intelligence , and radiomics , transitioning toward deep learning , precision medicine , and mHealth . Bradford’s Law identified 36 core journals, including Scientific Reports , BMJ Open , and PLOS ONE . Thematic evolution showed a shift from algorithmic and retrospective studies to clinically grounded themes such as cognitive behavioral therapy and telemedicine . Emerging topics emphasized translational and patient-centered applications. Conclusion This study delineates the dynamic evolution of ML in clinical trials, highlighting its growing integration into precision medicine. Future research should prioritize inclusivity, real-world implementation, and ethical frameworks to sustain equitable and clinically impactful innovation.