Published on in Vol 4 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66926, first published .
High-Throughput Phenotyping of the Symptoms of Alzheimer Disease and Related Dementias Using Large Language Models: Cross-Sectional Study

High-Throughput Phenotyping of the Symptoms of Alzheimer Disease and Related Dementias Using Large Language Models: Cross-Sectional Study

High-Throughput Phenotyping of the Symptoms of Alzheimer Disease and Related Dementias Using Large Language Models: Cross-Sectional Study

Journals

  1. Wister A, Pinigina E, Liang J, Linkov I. AI-enabled resilience modeling for brain health. Frontiers in Molecular Medicine 2025;5 View
  2. Wang B, Zhao T, Ma R, Huo X, Xiong X, Wu M, Wang Y, Liu L, Zhuang Z, Wang B, Shou J. Comparative Diagnostic Accuracy of AI-Assisted Fluorine-18 Fluorodeoxyglucose Positron Emission Tomography Versus Structural Magnetic Resonance Imaging in Alzheimer Disease: Systematic Review and Meta-Analysis. JMIR Aging 2025;8:e76981 View
  3. Chang E, Xie K, Ellis C. Transformer Language Models for Neurology Research with Electronic Health Records: Current State of the Science. Seminars in Neurology 2025 View

Conference Proceedings

  1. Li X, Jin X. 2025 19th International Conference on Complex Medical Engineering (CME). Recent Advances in Multimodal Integration for Alzheimer’s Disease Diagnosis: From Brain Imaging to Behavioral and Molecular Signatures View