ReviewAnti-inflammatory & anti-allergy agents in medicinal chemistry2026
Developments in the Management Strategies for Allergy: Advances in Artificial Intelligence and Future Perspectives.
Review in Anti-inflammatory & anti-allergy agents in medicinal chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Preface.Anti-inflammatory & anti-allergy agents in medicinal chemistry · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionArtificial intelligence (AI) is rapidly transforming biomedical research by offering advanced tools to analyse complex datasets. In the field of allergy studies, however, the translation of AI-generated insights into clinical practice remains limited and underutilised.
methodThis review critically discussed the current applications of AI in allergy studies. It focuses on the methodological foundations of AI, including machine learning and clustering algorithms, and assesses their practical benefits and limitations. Representative case studies are explored to demonstrate real-world applications, and challenges in data quality, integration, and algorithmic fairness are examined.
resultsAI techniques have shown promise in tasks such as disease phenotyping and patient stratification within allergy research. Case studies reveal that AI can uncover immunological insights and support precision medicine approaches. However, the field faces challenges, including fragmented data sources, algorithmic bias, and the limited presence of therapeutic AI tools in clinical practice. DISCUSSION: Despite the demonstrated potential, several barriers hinder the broader adoption of AI in allergy care. These include the need for high-quality, standardised datasets, ethical oversight, and transparent methodologies. The review highlights the importance of these factors in ensuring the reliability, reproducibility, and equity of AI-driven interventions in allergy research.
conclusionAI holds significant promise for improving diagnostic accuracy and enabling personalised treatment strategies in allergy care. Realising its full potential will require robust frameworks, ethical governance, and interdisciplinary collaboration to overcome current limitations and drive clinical translation.
Indexed as
Identifiers
40965022What OpenQuestion holds
Registered trials
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.