ReviewDigital health
The landscape of machine learning in clinical applications: A thematic mapping of evolution, frontiers, and future opportunities.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 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 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.