Hong Tan, Xuehua Zhou, Huajie Wu, Min Wang, Han Zhou, Yue Qin, Yun Zhang, Qiuhong Li, Jianfeng Luo, Hui Su, Xin Sun
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Application and research progress of artificial intelligence in allergic diseases.
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.
期刊介绍:
Original research papers, reviews, and short research communications in any medical related area can be submitted to the Journal on the understanding that the work has not been published previously in whole or part and is not under consideration for publication elsewhere. Manuscripts in basic science and clinical medicine are both considered. There is no restriction on the length of research papers and reviews, although authors are encouraged to be concise. Short research communication is limited to be under 2500 words.