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

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.