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

Journal: JMIR aging
PMID: 40231591

Abstract

BACKGROUND: The global increase in life expectancy has not shown a similar rise in healthy life expectancy. Accurate assessment of biological aging is crucial for mitigating diseases and socioeconomic burdens associated with aging. Current biological age prediction models are limited by their reliance on conventional statistical methods and constrained clinical information.

Authors

  • Chang-Uk Jeong
    Department of Software and Computer Engineering, Ajou University, Suwon, 16499, South Korea.
  • Jacob S Leiby
    Department of Biostatistics, Epidemiology and Informatics, The Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6116, USA.
  • Dokyoon Kim
    Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, USA.
  • Eun Kyung Choe
    Department of Surgery, Seoul National University Hospital Healthcare System Gangnam Center, 39FL Gangnam Finance Center 152, Teheran-ro, Gangnam-gu, Seoul, 135-984, South Korea. snuhcr@naver.com.

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