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The landscape of machine learning in clinical applications: A thematic mapping of evolution, frontiers, and future
Amir Mohamed Talib1, Siddig Ibrahim Abdelwahab2, Manal Mohamed Elhassan Taha2
1College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.
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.
