Artificial Intelligence-Driven Precision Medicine: A Review of Emerging Algorithms and Clinical Applications

The convergence of artificial intelligence (AI) with precision medicine has ushered in a transformative era in healthcare, facilitating highly individualized approaches to diagnostics, therapeutic strategies, and patient outcome forecasting. This comprehensive review examines the forefront of AI-driven innovations, spotlighting emerging algorithms such as deep learning, reinforcement learning, and generative models. It delves into their practical applications across critical domains, including genomics, medical imaging, and drug discovery, while evaluating their capacity to decode complex disease heterogeneity in conditions like cancer, neurodegenerative diseases, and rare genetic disorders. The analysis extends to a critical appraisal of these algorithms' strengths, such as enhanced predictive accuracy, alongside their limitations, including computational demands and interpretability challenges. Additionally, this review addresses pivotal ethical considerations, data privacy issues, and the imperative for rigorous validation frameworks to bridge the gap between research and widespread clinical implementation. By synthesizing the latest evidence, this article illuminates the current landscape and charts prospective pathways for AI-driven precision medicine to redefine patient-centered care.

Keywords:

Artificial Intelligence , Precision Medicine , Emerging Algorithms , Deep Learning , Reinforcement Learning