Evidence map›Paper›PMID 40303497›Full record

ReviewInternational journal of medical sciences2025

Application and research progress of artificial intelligence in allergic diseases.

Hong Tan, Xuehua Zhou, Huajie Wu, Min Wang, Han Zhou, Yue Qin, Yun Zhang, Qiuhong Li, Jianfeng Luo, Hui Su and 1 more

Abstract readReview
In one paragraph

Review in International journal of medical sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Review
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Hong TanDepartment of Pediatrics, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi, China.
Xuehua ZhouDepartment of Pediatrics, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi, China.
Huajie WuDepartment of Pediatrics, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi, China.
Min WangDepartment of Pediatrics, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi, China.
Han ZhouDepartment of Pediatrics, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi, China.
Yue QinDepartment of Pediatrics, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi, China.
Yun ZhangDepartment of Pediatrics, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi, China.
Qiuhong LiDepartment of Pediatrics, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi, China.
Jianfeng LuoDepartment of Pediatrics, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi, China.
Hui SuDepartment of Geriatrics, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi, China.
Xin SunDepartment of Pediatrics, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial IntelligenceHypersensitivityAsthmaDeep LearningHumansMachine LearningQuality of LifeAllergic diseasesArtificial intelligenceDiagnosis and predictionmanagement

Identifiers

PMID40303497
PMCPMC12035833

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.