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Review Open Access

Applications of machine learning and deep learning in precision medicine: Opportunities and challenges in genomics, oncology and clinical integration (Review)

  • Authors:
    • Qiang Zhao
    • Guangxin Li
    • Kunpeng Du
  • View Affiliations / Copyright

    Affiliations: Center for Precision Cancer Medicine and Translational Research, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, P.R. China, Department of Pathology, Chongqing University Cancer Hospital, Chongqing 400042, P.R. China, Department of Radiation Oncology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong 510282, P.R. China
    Copyright: © Zhao et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 192
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    Published online on: October 15, 2025
       https://doi.org/10.3892/br.2025.2070
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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.
View Figures

Figure 1

Four sequential processes of
generative ML in precision medicine are illustrated: Patient data
acquisition, ML-driven analysis, personalized treatment
implementation and outcome feedback, forming a closed-loop
optimization system. ML, machine learning.
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Copy and paste a formatted citation
Spandidos Publications style
Zhao Q, Li G and Du K: Applications of machine learning and deep learning in precision medicine: Opportunities and challenges in genomics, oncology and clinical integration (Review). Biomed Rep 23: 192, 2025.
APA
Zhao, Q., Li, G., & Du, K. (2025). Applications of machine learning and deep learning in precision medicine: Opportunities and challenges in genomics, oncology and clinical integration (Review). Biomedical Reports, 23, 192. https://doi.org/10.3892/br.2025.2070
MLA
Zhao, Q., Li, G., Du, K."Applications of machine learning and deep learning in precision medicine: Opportunities and challenges in genomics, oncology and clinical integration (Review)". Biomedical Reports 23.6 (2025): 192.
Chicago
Zhao, Q., Li, G., Du, K."Applications of machine learning and deep learning in precision medicine: Opportunities and challenges in genomics, oncology and clinical integration (Review)". Biomedical Reports 23, no. 6 (2025): 192. https://doi.org/10.3892/br.2025.2070
Copy and paste a formatted citation
x
Spandidos Publications style
Zhao Q, Li G and Du K: Applications of machine learning and deep learning in precision medicine: Opportunities and challenges in genomics, oncology and clinical integration (Review). Biomed Rep 23: 192, 2025.
APA
Zhao, Q., Li, G., & Du, K. (2025). Applications of machine learning and deep learning in precision medicine: Opportunities and challenges in genomics, oncology and clinical integration (Review). Biomedical Reports, 23, 192. https://doi.org/10.3892/br.2025.2070
MLA
Zhao, Q., Li, G., Du, K."Applications of machine learning and deep learning in precision medicine: Opportunities and challenges in genomics, oncology and clinical integration (Review)". Biomedical Reports 23.6 (2025): 192.
Chicago
Zhao, Q., Li, G., Du, K."Applications of machine learning and deep learning in precision medicine: Opportunities and challenges in genomics, oncology and clinical integration (Review)". Biomedical Reports 23, no. 6 (2025): 192. https://doi.org/10.3892/br.2025.2070
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