AI-driven transformation of precision medicine: a comprehensive narrative review of key application areas, emerging paradigms, and future directions

作者：Qin Zeng, Cheng Huang, Jun Zhu · 发表于：Frontiers in Public Health · 年份：2026 · DOI：10.3389/fpubh.2025.1656603 · 被引用次数：9 · 研究领域：Artificial Intelligence in Healthcare and Education、Machine Learning in Healthcare、COVID-19 diagnosis using AI

Objectives: This study aims to elucidate the pivotal role of Artificial Intelligence (AI) in driving the transformation of precision medicine, comprehensively analyzing how it reshapes healthcare systems from traditional diagnosis and treatment paradigms into personalized health management ecosystems. Methods: A comprehensive narrative review was conducted to systematically synthesize and critically evaluate the innovative applications, paradigm shifts, and future prospects of AI across the entire precision medicine value chain. A comprehensive literature search was performed across multiple databases up to April 30, 2025, with a focus on the clinical implementation and breakthroughs of technologies such as deep learning (DL), machine learning (ML), and natural language processing (NLP). Results: AI technologies have significantly enhanced the accuracy and efficiency of disease diagnosis through medical image analysis, genomics, and multimodal data fusion. At the treatment level, AI enables the development of personalized therapeutic plans and drug dosing optimization, while revolutionarily accelerating the drug development pipeline from discovery to clinical trials. Integrated with wearable devices and telemedicine platforms, AI facilitates full-cycle health monitoring. However, the clinical translation of AI faces challenges, including an uneven evidence base, insufficient model generalizability, and ethical concerns regarding data privacy, algorithmic fairness, and interpr...