Evidence map›Paper›PMID 40851769›Full record

ReviewJournal of asthma and allergy2025

Artificial Intelligence in the Management of Asthma: A Review of a New Frontier in Patient Care.

Laren D Tan, Nolan Nguyen, Enrique Lopez, Daniel Peverini, Mathew Shedd, Abdullah Alismail, H Bryant Nguyen

Abstract readReview
In one paragraph

Review in Journal of asthma and allergy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Artificial Intelligence and the future of clinical trials.Contemporary clinical trials communications · 2025
    Article
  9. 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

7 authors.

Laren D TanDepartment of Medicine, Loma Linda University Health, Loma Linda, CA, USA.ORCID 0000-0002-1974-1250
Nolan NguyenSchool of Medicine, Loma Linda University Health, Loma Linda, CA, USA.
Enrique LopezDepartment of Medicine, Loma Linda University Health, Loma Linda, CA, USA.
Daniel PeveriniDepartment of Medicine, University of New Mexico Consortium of the Americas for Interdisciplinary Science, Albuquerque, NM, USA.
Mathew SheddDepartment of Medicine, Loma Linda University Health, Loma Linda, CA, USA.
Abdullah AlismailDepartment of Medicine, Loma Linda University Health, Loma Linda, CA, USA.ORCID 0000-0002-7844-8943
H Bryant NguyenDepartment of Medicine, Loma Linda University Health, Loma Linda, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Asthma, a chronic respiratory condition, impacts over 339 million individuals globally, including 25 million in the United States, contributing to significant morbidity and healthcare costs. Despite advances, challenges persist in managing exacerbations, ensuring medication adherence, and patient education. This narrative review explores the transformative potential of artificial intelligence (AI) in improving asthma management through predictive analytics, personalized treatment, and continuous patient engagement. A search of the United States National Library of Medicine's PubMed database was performed for articles pertaining to asthma and artificial intelligence, machine learning (ML), neural network, or deep learning. The current research on AI applications in asthma care was then reviewed, including algorithms, AI-driven tools for personalized medicine, and digital platforms for patient engagement. Case studies and clinical trials assessing AI's impact on predictive accuracy and treatment adherence were reviewed. AI, particularly ML, enhances asthma management by analyzing data from wearables and patient records to predict exacerbations, stratify risk, and inform personalized treatment. Studies demonstrate AI's capability to recommend tailored interventions, monitor adherence through smart applications, and facilitate real-time treatment adjustments. Ethical challenges include ensuring patient trust, data security, and equitable technology access. In conclusion, AI's integration in asthma care holds significant promise for predictive interventions, personalized regimens, and continuous support, ultimately aiming to improve patient outcomes and reduce healthcare burdens. Continued advancements in AI will bridge current care gaps, fostering a patient-centric, proactive approach in asthma management.

Indexed as

artificial intelligenceasthmamachine learningpersonalized medicine

Identifiers

PMID40851769
PMCPMC12367921

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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 OpenQuestion graph.