Journal of Preventive Medicine and Public Health

Review

The Next Frontiers in Preventive and Personalized Healthcare: Artificial Intelligent-powered Solutions: Rasit Dinc, Nurittin Ardic; J Prev Med Public Health. 2025;58(5):441-452. Published online May 29, 2025; DOI: https://doi.org/10.3961/jpmph.25.080

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Artificial intelligence (AI)-enabled technologies have the potential to significantly increase diagnostic accuracy, optimize treatment strategies, and improve patient outcomes. They are revolutionizing the field of preventive and personalized medicine by providing data-driven insights. AI is capable of analyzing large and complex datasets such as genomic, environmental, and lifestyle information much faster and more conveniently than traditional methods. Advanced algorithmic architectures in AI can predict disease risks, identify biomarkers, and tailor interventions to individual needs. The enabling role of AI in real-time monitoring, predictive analysis, and drug discovery demonstrates its transformative potential in healthcare. The role of AI in multi-omics integration, wearable technologies, and precision therapies promises to redefine global healthcare paradigms, making personalized medicine more accessible and effective. However, ethical concerns that need to be addressed to ensure fair and transparent implementation include data privacy, algorithmic bias, and regulatory gaps. This article examines the integration of AI technologies with personalized healthcare. The study also highlights the need for interdisciplinary collaboration to maximize the benefits of AI in preventive and personalized healthcare and overcome barriers.

Summary

Key Message
Artificial intelligence significantly accelerates preventive and personalized medicine by analyzing complex genomic, environmental, and lifestyle datasets to predict disease risks, identify biomarkers, and tailor interventions to individual needs. Through real-time monitoring, predictive analysis, and precision therapies, AI-enabled technologies play a critical role in increasing diagnostic accuracy, optimizing treatment strategies, and improving patient outcomes. However, successful implementation requires addressing critical challenges such as data privacy concerns, algorithmic bias, regulatory gaps, and the need for interdisciplinary collaboration to provide equitable, transparent, and accessible AI-enabled healthcare solutions.

Citations

Citations to this article as recorded by

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