Evidence map›Paper›PMID 41052724›Full record

ReviewThe journal of allergy and clinical immunology. In practice2025

Artificial Intelligence-Driven Wearable and Connected Technology for Allergy: Real-Time Monitoring and Predictive Management for Personalized Care.

George N Konstantinou, William C Anderson, Evangelos Bagkis, Zoe Brown, Kostas Karatzas, Theodosios Kassandros, Chrysanthi Sardeli, Ruchi Singla, Sharmilee M Nyenhuis

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in The journal of allergy and clinical immunology. In practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

9 authors.

George N KonstantinouDepartment of Allergy and Clinical Immunology, 424 General Military Training Hospital, Thessaloniki, Greece. Electronic address: gnkonstantinou@gmail.com.
William C AndersonDepartment of Pediatrics, Section of Allergy and Immunology, University of Colorado, Anschutz Medical Campus, Aurora, Colo.
Evangelos BagkisEnvironmental Informatics Research Group, School of Mechanical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Zoe BrownDepartment of Pediatrics, Section of Allergy and Immunology, University of Chicago, Chicago, Ill.
Kostas KaratzasEnvironmental Informatics Research Group, School of Mechanical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Theodosios KassandrosEnvironmental Informatics Research Group, School of Mechanical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Chrysanthi SardeliLaboratory of Clinical Pharmacology, School of Medicine and Laboratory for the Study of Law and Bioethics, Faculty of Law, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Ruchi SinglaDepartment of Pediatrics, Section of Allergy and Immunology, University of Chicago, Chicago, Ill.
Sharmilee M NyenhuisDepartment of Pediatrics, Section of Allergy and Immunology, University of Chicago, Chicago, Ill.

Funding

ChicAgo Center for Health and EnvironmenT (CACHET)P30ES027792 · UNIVERSITY OF CHICAGO · 2025 to 2025
$1.6M
NIEHS NIH HHS P30 ES027792
6 · The paper itself

Abstract

Allergic diseases are increasing worldwide, underscoring the need for innovative management strategies. Wearable and connected technologies combined with artificial intelligence (AI) can support real-time monitoring, personalized alerts, and proactive interventions. This review summarizes AI-enabled tools for allergy care spanning physiologic signals, environmental exposures (eg, pollutant proxies such as particulates and volatile organic compounds), and patient behaviors, as well as connected medication-adherence technologies (eg, digital inhalers) that integrate with the same analytics workflows. We also outline predictive algorithms that forecast exacerbations and briefly review therapeutic devices. Reported benefits include earlier warning of clinical deterioration, improved adherence and technique, and opportunities for tailored management. However, important limitations remain regarding data accuracy and reliability, user adoption, workflow integration, equity and fairness, privacy and cybersecurity, and evolving regulatory pathways. Critically, most devices and algorithms reviewed are investigational or early-phase, with evidence dominated by feasibility or short-term studies, and only a few show improvements in patient-centered outcomes in prospective trials. Realizing clinical value will require outcome-focused validation (including external and pragmatic studies), safeguards for privacy and security, attention to bias and subgroup performance, and implementation models that fit clinical workflows and reimbursement. With these conditions met, AI-driven wearable and connected technologies could enable more proactive, personalized allergy care.

Indexed as

Artificial IntelligenceHypersensitivityPrecision MedicineWearable Electronic DevicesHumansArtificial intelligenceEnvironmental exposureInhalation devicesMachine learningMedication adherencePrecision medicineRemote monitoringWearable electronic devices

Identifiers

PMID41052724
PMCPMC13229188

What Socratic holds

Textmetadata
LicenceTDM
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.