ReviewBiomedical reports2025
Applications of machine learning and deep learning in precision medicine: Opportunities and challenges in genomics, oncology and clinical integration (Review).
Review in Biomedical reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Knowledge graph visualization and retrospective analysis of genetic research on pediatric cardiomyopathy (2000-2024).Frontiers in cardiovascular medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the advancement of precision medicine, machine learning (ML) and deep learning have increasingly become a pivotal tool for driving medical innovation. Precision medicine, grounded in individual variability, aims to deliver personalized treatment interventions, with ML serving as a critical enabler for achieving this goal. Recent ML-driven progress in genomic analysis, personalized treatment optimization and disease diagnostics have significantly elevated the accuracy and efficacy of medical decision-making processes. However, the widespread adoption of artificial intelligence also faces multifaceted challenges, including data privacy frameworks, cybersecurity risks, ethical considerations and the integration of technology with clinical workflows. The present review seeks to analyze cutting-edge applications of ML within precision medicine domains, examine its challenges, and project future evolutionary pathways, emphasizing the critical need for proactive attention to these issues to ensure tangible benefits for patients and healthcare systems.
Indexed as
Identifiers
What OpenQuestion holds
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.