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This paper is in the following e-collection/theme issue:

Digital Health Reviews (3824) Mobile Health (mhealth) (5291) Dementia and Cognitive Decline (913) Wearable Devices and Sensors (1648) Digital Biomarkers and Digital Phenotyping (469) Artificial Intelligence (4975) Computer-Aided Diagnosis (439)

Published on in Vol 28 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/86262, first published .
Doctor examines brain scan data on tablet, smartwatch displays vital signs

AI and Wearables for Early Detection of Cognitive Impairment and Dementia: Systematic Review

AI and Wearables for Early Detection of Cognitive Impairment and Dementia: Systematic Review

Authors of this article:

Ander Cejudo1, 2 Author Orcid Image ;   Markel Arrojo1 Author Orcid Image ;   Cristina Martín1, 2, 3 Author Orcid Image ;   Aitor Almeida2 Author Orcid Image
Article Authors Cited by (11) Tweetations (2) Metrics

Journals

  1. Jung D, Seo H, Yoo L. Artificial intelligence in nursing practice for older adults with dementia: A narrative review informed by bibliometric mapping and implications for nurse-led research. Journal of Korean Gerontological Nursing 2026;28(2):125 View
  2. Kim H, Park J. Longitudinal Variability of Wearable-Derived Sleep and Heart Rate as a Digital Biomarker for Early Detection of Mild Cognitive Impairment. Yonsei Medical Journal 2026;67(7):524 View
  3. Garg S, Walters B, Fernandez D. Chronobiomaterials for circadian-aligned brain therapeutics. Trends in Pharmacological Sciences 2026;47(7):802 View
  4. Lin J, Kuok K, Mio W, Tan C, Tou P, Cheang C. Beyond single-modality screening: toward an EEG–IoT dual-stream framework for early detection of mild cognitive impairment in community-dwelling older adults. Frontiers in Aging Neuroscience 2026;18 View
  5. 黄 乐. Application and Research Progress of Digital Biomarkers Related to Alzheimer’s Disease. Open Journal of Natural Science 2026;14(05):535 View
  6. Ianculescu M, Băjenaru L, Săndulescu V, Petean C. A Standards-Mapped Accessibility-by-Design Framework for AI-Driven Smart Healthcare Solutions: Application to the NeuroPredict Platform. Information 2026;17(9):850 View
  7. Rabou M, Tawfik H, Ismail N, Attiya G, El-Sheikh G, Abdulrahman S. Machine learning-based multiclass classification of cognitive decline using ACE-III assessment data: a comparative study with SHAP explainability. Scientific Reports 2026;16(1) View
  8. Holfelder A, de Terrasson de Montleau A, Vinay R, Nißen M, Klöppel S, Kowatsch T, Brill E. Implementation of Sensor-Augmented Conversational Agent for Personalized Lifestyle Support in Older Adults with Mild Cognitive Impairment: Two-Week Feasibility Study (Preprint). JMIR Formative Research 2026 View

Books/Policy Documents

  1. S. M. F, Kamalakkannan S. Intelligent IoT and Assistive Systems for Neurocognitive Healthcare. View
  2. Kurapati J. Intelligent IoT and Assistive Systems for Neurocognitive Healthcare. View

Conference Proceedings

  1. Chai Z, Zhou Y, Yu H. 2026 31st International Conference on Automation and Computing (ICAC). AI-Driven Wearable Sensor Analytics for Dementia Monitoring: Machine Learning, Deep Learning, and Emerging Foundation-Model Opportunities View