ReviewAllergy2026
Leveraging Artificial Intelligence in Allergy, Asthma, and Immunology With Environmental Exposures.
Review in Allergy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
24 authors.
Funding
Abstract
Artificial intelligence (AI) in environmental health science is revolutionizing data analysis and problem-solving approaches. These technologies facilitate the prediction of environmental exposures and disease outcomes and enable the identification of causal relationships for subsequent hypothesis testing. AI techniques improve pollution research through the analysis of satellite imagery and the modeling of pollutant dispersion, while AI advances chemical safety evaluations in toxicology by examining extensive datasets. AI is instrumental in addressing pressing environmental challenges, including remediation of polluted sites and ensuring equitable healthcare applications to mitigate biases. The expanding availability of large-scale environmental, geospatial, and health outcome databases offers unprecedented opportunities for innovative applications. Their predictive capabilities are essential in disaster management, enabling real-time analysis and optimizing resource deployment amid climate-related crises. AI-driven approaches play a critical role in carbon capture and waste management efforts aimed at reducing environmental impact. Furthermore, AI can elucidate complex relationships between the exposome-defined as the totality of exposures throughout an individual's life-and health outcomes, facilitating preventative strategies. This review examines the capabilities and limitations of AI in environmental health and safety, providing insights into its judicious and effective use for environmental management and healthcare.
Indexed as
Identifiers
What 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.