[AI-driven PET-MRI multimodal fusion: paradigm shift and clinical translation challenges in precision diagnosis and treatment of neurodegenerative diseases].

Individualized precision diagnosis and treatment of neurodegenerative diseases (Alzheimer's disease, Parkinson's disease, etc.) faces challenges due to overlapping early symptoms and clinical/pathological heterogeneity. PET-MRI multimodal imaging, integrating in vivo molecular pathological information from PET with brain structural/functional information revealed by MRI, has become a crucial cornerstone for precision diagnosis and treatment of neurodegenerative diseases. However, its clinical translation is limited by practical bottlenecks, such as complexities in data integration, and uneven distribution of resources. AI, with its unique strengths in multimodal data fusion, automated quantitative analysis, and cross-modal image synthesis, is gradually reshaping the paradigm of diagnostic and therapeutic landscape of neurodegenerative diseases. This article systematically explores the pivotal role of AI in PET-MRI, covering its contributions to improving diagnostic objectivity, deciphering disease heterogeneity, enabling stratified care pathways. It also critically addresses the multiple challenges hindering the clinical implementation of AI and proposes that future efforts should focus on the development of interpretable AI models, the construction of embedded clinical systems, and the exploitation of inclusive technological solutions to promote the deep integration of AI and PET-MRI, ultimately driving the transformation of neurodegenerative diseases towards a precision medicine paradigm of early prevention, early diagnosis, and early treatment.