Accessibility settings

Published on in Vol 4 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66926, first published .
AI analyzing clinical notes for memory, executive function, language, motor, sleep, and visuospatial skills.

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 2026;46(01):026 View
  4. Ali M, Zafar A, Masood H, Dong J, Lee S. A review of the emergence of AI-driven cognitive digital phenotyping: Multimodal sensing, foundation models, and the path toward digital twins for human cognition. Computer Science Review 2026;62:100994 View
  5. Chong A, Sasmita A, Koh R, Ling A. Neuroprotective effects of ursodeoxycholic acid in Parkinson's disease and Alzheimer's disease. Neuroprotection 2026 View
  6. Elkattawy H, Balfas M, Alnassar S, Mohsen A, Rajab F. Cracking the Code: Performance Convergence and Reasoning Blind Spots of Large Language Models on Alzheimer’s Questions in Medical Licensing Examinations. Advanced Health Science and Technology Journal 2026;2(1):97 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