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Abstract
The integration of artificial intelligence (AI) into personalized medicine is rapidly transforming healthcare by enabling more precise, predictive, and individualized patient care. This narrative review explores the clinical applications, benefits, and limitations of AI-powered personalized medicine across multiple disciplines, including oncology, radiology, rheumatology, dermatology, ophthalmology, and pharmacology. We provide an overview of foundational AI technologies such as machine learning, deep learning, and natural language processing, highlighting their role in optimizing diagnosis, predicting treatment responses, and supporting clinical decision-making. Key examples include AI-based image interpretation in radiology, predictive models for drug metabolism in pharmacogenomics, and generative AI tools for clinical documentation and drug discovery. We also examine large-scale biomedical databases such as the UK Biobank and the Cancer Genome Atlas, which serve as critical resources for developing AI-driven models in personalized care. Although AI demonstrates substantial potential in improving healthcare delivery, challenges remain, including concerns about clinical validity, reproducibility, data bias, and the interpretability of AI models. Furthermore, ethical considerations such as data privacy, algorithmic transparency, and equitable access to AI-guided interventions are increasingly significant. This review underscores the importance of interdisciplinary collaboration and robust validation frameworks to ensure the safe, effective, and equitable integration of AI into precision medicine. By synthesizing current literature and highlighting emerging trends, we aim to inform clinicians, researchers, and policymakers on how AI can be harnessed to realize the full potential of truly personalized healthcare.
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Article Type: Review Article
J CLIN EXP INVEST, Volume 17, Issue 2, June 2026, Article No: em00858
https://doi.org/10.29333/jcei/18276
Publication date: 01 Apr 2026
Online publication date: 30 Mar 2026
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Article Downloads: 3
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