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

Information and Patient Education on Healthy Aging (55) AI in Older Adult Care (42) Theme Issue 2025: Social and Cultural Drivers of Health in Aging Populations (8) Artificial Intelligence (2197) Machine Learning (2129) Digital Biomarkers and Digital Phenotyping (297)

Published on in Vol 8 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/64473, first published .
Artificial Intelligence-Driven Biological Age Prediction Model Using Comprehensive Health Checkup Data: Development and Validation Study

Artificial Intelligence-Driven Biological Age Prediction Model Using Comprehensive Health Checkup Data: Development and Validation Study

Artificial Intelligence-Driven Biological Age Prediction Model Using Comprehensive Health Checkup Data: Development and Validation Study

Authors of this article:

Chang-Uk Jeong1 Author Orcid Image ;   Jacob S Leiby1 Author Orcid Image ;   Dokyoon Kim1, 2 Author Orcid Image ;   Eun Kyung Choe3, 4 Author Orcid Image
Article Authors Cited by (2) Tweetations (4) Metrics

Journals

  1. You G, Wang K, Shen R, Chen X, Jiang J, Sun Y, Wu D, Xu J, Huang K, Yao C. Metabolomic aging clock predicts risk of different cardiovascular diseases in the UK Biobank. Metabolism 2026;176:156467 View
  2. Anagnostopoulos T, Zervoudi E, Anagnostopoulos C, Christopoulos A, Wierzbinski B. An arithmetic method algorithm optimizing k-nearest neighbors compared to regression algorithms and evaluated on real world data sources. Scientific Reports 2026 View