ReviewCurrent allergy and asthma reports2025
AI-Driven Biomarker Discovery and Personalized Allergy Treatment: Utilizing Machine Learning and NGS.
Review in Current allergy and asthma reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Machine Learning-Based Prediction of Food Allergy in Children Aged <2 Years with Atopic Dermatitis.Journal of clinical medicine · 2026Article
- Body Biofluids for Minimally-Invasive Diagnostics: Insights, Challenges, Emerging Technologies, and Clinical Potential.Advanced healthcare materials · 2026Review
- Digital health in allergy care: current practices, evidence, and future prospects.Frontiers in allergy · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
PURPOSE OF REVIEW: This review explores the transformative potential of artificial intelligence (AI) and next-generation sequencing (NGS) in allergy diagnostics and treatment. It focuses on leveraging these technologies to enhance precision in biomarker discovery, patient stratification, and personalized management strategies for allergic diseases. RECENT
findingsAI-driven algorithms, particularly machine learning and deep learning, have enabled the identification of complex molecular patterns and predictive markers in allergies, such as IgE levels and cytokine profiles. Integration with NGS techniques, including single-cell RNA sequencing, has uncovered unique immune response signatures, providing insights into molecular mechanisms driving allergic reactions. These innovations have advanced diagnostic accuracy, treatment personalization, and real-time monitoring capabilities, especially in allergen immunotherapy. Combining AI and NGS technologies represents a paradigm shift in allergy research and clinical practice. These advancements facilitate precision diagnostics and personalized treatments, ensuring safer and more effective interventions tailored to individual patient profiles. Despite data integration and clinical implementation challenges, these technologies promise improved outcomes and quality of life for allergy sufferers.
Indexed as
Identifiers
40459653What Socratic 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 Socratic graph.