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

Digital Health Reviews (1551) Artificial Intelligence (1773) Machine Learning (1769) Big Data Analytics for Elder Care (28) Clinical Information and Decision Making (1686) Computer-Aided Diagnosis (188)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47346, first published .
Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review

Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review

Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review

Authors of this article:

Daniel Velazquez-Diaz1, 2 Author Orcid Image ;   Juan E Arco3, 4, 5 Author Orcid Image ;   Andres Ortiz3, 4 Author Orcid Image ;   Verónica Pérez-Cabezas6, 7 Author Orcid Image ;   David Lucena-Anton7, 8 Author Orcid Image ;   Jose A Moral-Munoz7, 8 Author Orcid Image ;   Alejandro Galán-Mercant6, 7 Author Orcid Image
Article Authors Cited by (12) Tweetations (6) Metrics

Journals

  1. Vahia I. Navigating New Realities in Aging Care as Artificial Intelligence Enters Clinical Practice. The American Journal of Geriatric Psychiatry 2024;32(3):267 View
  2. Clarnette R, Kostov I, Ryan J, Svendrovski A, Molloy D, O’Caoimh R. Predicting Outcomes in Frail Older Community-Dwellers in Western Australia: Results from the Community Assessment of Risk Screening and Treatment Strategies (CARTS) Programme. Healthcare 2024;12(13):1339 View
  3. Trott V, Holthaus M. Patient Education and Special Populations. Physician Assistant Clinics 2024;9(4):567 View
  4. Kim D. Unleashing frailty from laboratory into real world: A critical step toward frailty‐guided clinical care of older adults. Journal of the American Geriatrics Society 2024;72(11):3299 View
  5. Yamashita M, Kamiya K, Hotta K, Kubota A, Sato K, Maekawa E, Miyata H, Ako J. Artificial Intelligence (AI)-Driven Frailty Prediction Using Electronic Health Records in Hospitalized Patients With Cardiovascular Disease. Circulation Reports 2024;6(11):495 View
  6. Blaß M, Gimpel H, Karnebogen P. A Taxonomy and Archetypes of AI-Based Health Care Services: Qualitative Study. Journal of Medical Internet Research 2024;26:e53986 View
  7. Zhang L, Li J. Prospects for the application of artificial intelligence in geriatrics. Journal of Translational Internal Medicine 2025;12(6):531 View
  8. Cham S, Kumar A, Walter L, Lichtman S, Tew W. Understanding frailty and the role of patient-centered care for older adults with gynecologic cancer. Gynecologic Oncology 2025;196:42 View
  9. Schoenborn N, Chae K, Massare J, Ashida S, Abadir P, Arbaje A, Unberath M, Phan P, Cudjoe T. Perspectives on AI and Novel Technologies Among Older Adults, Clinicians, Payers, Investors, and Developers. JAMA Network Open 2025;8(4):e253316 View
  10. Rony M, Deb T, Khatun M, Nesa L, Begum J, Afrin M, Noor M, Akter M, Khatun M, Begum M, Parvin M, Akter F. Attitudes of older patients toward artificial intelligence in decision-making in healthcare. Journal of Medicine, Surgery, and Public Health 2025;6:100193 View
  11. Isaradech N, Sirikul W, Buawangpong N, Siviroj P, Kitro A. Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study. JMIR Aging 2025;8:e62942 View
  12. Ortiz-Navarro B, Losa-Reyna J, Mihaiescu-Ion V, Garcia-Romero J, Carrillo de Albornoz-Gil M, Galán-Mercant A. Identification of Target Body Composition Parameters by Dual-Energy X-Ray Absorptiometry, Bioelectrical Impedance, and Ultrasonography to Detect Older Adults With Frailty and Prefrailty Status Using a Mobile App in Primary Care Services: Descriptive Cross-Sectional Study. JMIR Aging 2025;8:e67982 View