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Volume 42 Issue 10
Oct.  2026
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Article Navigation >  CHINESE JOURNAL OF BURNS AND WOUNDS  > 2026  >  42(10): 997-1002
Wang G,Qian HY,Yue XM.Research advances on the application of artificial intelligence in the warning and management of burn-related delirium[J].Chin J Burns Wounds,2026,42(10):997-1002.DOI: 10.3760/cma.j.cn501225-20250911-00392.
Citation: Wang G,Qian HY,Yue XM.Research advances on the application of artificial intelligence in the warning and management of burn-related delirium[J].Chin J Burns Wounds,2026,42(10):997-1002.DOI: 10.3760/cma.j.cn501225-20250911-00392.
shu

Research advances on the application of artificial intelligence in the warning and management of burn-related delirium

doi: 10.3760/cma.j.cn501225-20250911-00392
  • 1.

    Department of Intensive Care Medicine, Jiaxing Hospital of Traditional Chinese Medicine, Jiaxing 314000, China

  • 2.

    Department of Neurosurgery, Jiaxing Hospital of Traditional Chinese Medicine, Jiaxing 314000, China

Funds:

Zhejiang Provincial Traditional Chinese Medicine Science and Technology Plan 2024ZL1070

More Information
  • Corresponding author: Wang Gui, Email: wg1050@163.com
  • Received Date: 2025-09-11
    Available Online: 2026-10-26
    Abstract
  • Delirium in burn patients presented a prominent challenge in intensive care, characterized by high incidence, difficult identification, and a narrow intervention window. Artificial intelligence (AI) technology, with its unique advantages of being adept at processing complex high-dimensional data, mining hidden patterns, and real-time continuous monitoring, provided new directions for the warning and management of burn-related delirium. Based on the main driving mechanisms and clinical heterogeneity of burn-related delirium, this article systematically reviewed the research advances of AI in establishment of predictive models for burn-related delirium, multimodal data fusion and interpretable outputs, multi-source data acquisition, intelligent processing and decision support, as well as clinical applications and closed-loop feedback. It analyzed current challenges such as data governance and algorithmic fairness, integration and collaboration in clinical scenarios, and liability attribution and ethical supervision, and prospected future research directions, aiming to provide references and insights for the application of AI in the field of burns.

     

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