• Artificial Intelligence and Machine Learning in Precision Medicine: Opportunities, Challenges, and Future Perspectives
  • Simin Adlkhah,1,*
    1. Islamic Azad University Tabriz Branch


  • Introduction: Abstract (with Introduction & Objectives): Precision medicine is an emerging approach in healthcare that tailors prevention, diagnosis, and treatment strategies to individual patient characteristics. The integration of artificial intelligence (AI) and machine learning (ML) enables the analysis of complex biomedical data to uncover hidden patterns, predict disease risk, and optimize therapies. This review aims to systematically evaluate the applications of AI and ML in precision medicine, identify key challenges in clinical implementation, and discuss future perspectives for improving individualized patient care. Recent studies demonstrate that AI and ML can enhance predictive modeling, medical imaging, drug optimization, and clinical decision-making, ultimately improving patient outcomes.
  • Methods: Methods This review was conducted by systematically searching major scientific databases, including PubMed, Scopus, and Web of Science, for articles published between 2015 and 2025. The keywords used included “artificial intelligence,” “machine learning,” “precision medicine,” “personalized medicine,” “predictive modeling,” and “clinical decision support.” Additional relevant references were identified by manually screening the bibliographies of retrieved articles. Original research papers, systematic reviews, and meta-analyses that addressed the application of AI and ML in precision medicine were included. Studies focusing solely on non-medical domains or lacking peer review were excluded. All included articles were evaluated for relevance, quality, and contribution to the field. The information extracted from these sources was synthesized to provide an overview of current applications, challenges, and future directions of AI and ML in precision medicine.
  • Results: Results The review of the recent literature indicates that artificial intelligence (AI) and machine learning (ML) have rapidly expanded into various domains of precision medicine. Significant progress has been achieved in genomics, where AI-based models support variant interpretation, gene–disease association mapping, and patient stratification. In medical imaging, deep learning algorithms have outperformed conventional methods in detecting early disease manifestations, particularly in oncology, cardiology, and neurology. AI-driven predictive models are increasingly being used to forecast therapeutic responses, optimize drug dosing, and identify adverse events before they occur. Furthermore, integrated platforms combining multi-omics data with electronic health records show promising results in developing individualized treatment plans. Despite these advances, the literature also highlights persistent barriers, including data heterogeneity, algorithm transparency, and ethical issues related to patient privacy and bias.
  • Conclusion: Conclusion: Artificial intelligence (AI) and machine learning (ML) are rapidly transforming precision medicine by enabling data-driven insights, predictive modeling, and individualized patient care. Current applications in genomics, medical imaging, and therapeutic optimization demonstrate significant potential to improve clinical outcomes. However, challenges such as data heterogeneity, model interpretability, ethical concerns, and regulatory issues must be addressed to ensure safe and equitable implementation. Continued collaboration among clinicians, data scientists, and policymakers will be essential for realizing the full benefits of AI and ML in precision medicine. As these technologies evolve, they are likely to advance healthcare toward more predictive, preventive, personalized, and participatory approaches.
  • Keywords: Keywords: Artificial Intelligence Machine Learning Precision Medicine Predictive Modeling