Generative AI Models in Time-Varying Biomedical Data: Scoping Review.

Journal: Journal of medical Internet research
PMID: 40063929

Abstract

BACKGROUND: Trajectory modeling is a long-standing challenge in the application of computational methods to health care. In the age of big data, traditional statistical and machine learning methods do not achieve satisfactory results as they often fail to capture the complex underlying distributions of multimodal health data and long-term dependencies throughout medical histories. Recent advances in generative artificial intelligence (AI) have provided powerful tools to represent complex distributions and patterns with minimal underlying assumptions, with major impact in fields such as finance and environmental sciences, prompting researchers to apply these methods for disease modeling in health care.

Authors

  • Rosemary He
    Department of Computer Science, UCLA, Los Angeles, CA, USA.
  • Varuni Sarwal
    Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, United States.
  • Xinru Qiu
    Division of Biomedical Sciences, University of California Riverside School of Medicine, Riverside, CA 92521, USA.
  • Yongwen Zhuang
    Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States.
  • Le Zhang
    State Key Laboratory of Multiphase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China; College of Chemical Engineering, University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of Science and Technology on Particle Materials, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 361021, China.
  • Yue Liu
    School of Athletic Performance, Shanghai University of Sport, Shanghai, China.
  • Jeffrey Chiang
    Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, United States.

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