Application and research progress of artificial intelligence in allergic diseases.

Journal: International journal of medical sciences
PMID:

Abstract

Artificial intelligence (AI), as a new technology that can assist or even replace some human functions, can collect and analyse large amounts of textual, visual and auditory data through techniques such as Reinforcement Learning, Machine Learning, Deep Learning and Natural Language Processing to establish complex, non-linear relationships and construct models. These can support doctors in disease prediction, diagnosis, treatment and management, and play a significant role in clinical risk prediction, improving the accuracy of disease diagnosis, assisting in the development of new drugs, and enabling precision treatment and personalised management. In recent years, AI has been used in the prediction, diagnosis, treatment and management of allergic diseases. Allergic diseases are a type of chronic non-communicable disease that have the potential to affect a number of different systems and organs, seriously impacting people's mental health and quality of life. In this paper, we focus on asthma and summarise the application and research progress of AI in asthma, atopic dermatitis, food allergies, allergic rhinitis and urticaria, from the perspectives of disease prediction, diagnosis, treatment and management. We also briefly analyse the advantages and limitations of various intelligent assistance methods, in order to provide a reference for research teams and medical staff.

Authors

  • Hong Tan
    Department of Pediatrics, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi, China.
  • Xuehua Zhou
    Department of Pediatrics, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi, China.
  • Huajie Wu
    Department of Pediatrics, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi, China.
  • Min Wang
    National and Local Joint Engineering Research Center of Ecological Treatment Technology for Urban Water Pollution, Wenzhou University, Wenzhou 325035, China.
  • Han Zhou
    Jiangsu Provincial Key Laboratory of Special Robot Technology, Hohai University, Changzhou, China.
  • Yue Qin
    College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, Shandong 266590, China.
  • Yun Zhang
    Biology Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China.
  • Qiuhong Li
    National Health Commission (NHC) Key Laboratory of Hormones and Development, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China.
  • Jianfeng Luo
    Department of Pediatrics, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi, China.
  • Hui Su
    Department of Geriatrics, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi, China.
  • Xin Sun
    Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA.