AI-Driven Transformation of Precision Medicine: A Comprehensive Narrative Review of Key Application Areas, Emerging Paradigms, and Future Directions

Qin Zeng^1,2Huang Cheng^1,3Jun Zhu^4*

• ¹West China Second University Hospital, Sichuan University, Chengdu, China
• ²Sichuan University West China Second University Hospital Department of Nursing, Chengdu, China
• ³West China Hospital of Sichuan University, Chengdu, China
• ⁴Sichuan University West China Second University Hospital, Chengdu, China

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 interpretability. Conclusion: AI is a key driver of paradigm shift in precision medicine. To address existing challenges, future efforts should focus on generating more robust clinical evidence, adopting technologies like federated learning to ensure data privacy, and promoting the human-centered, collaborative framework of Symbiotic AI (SAI). By establishing sound ethical and governance structures, the deployment of AI technologies can be ensured to be not only efficient and advanced but also equitable and trustworthy, ultimately paving the way for an intelligent and inclusive healthcare ecosystem.