Exploration of Asthma & Allergy

Table 1. AI-enabled tools and platforms for pollen forecasting and patient monitoring.

Tool/Platform	Data sources	Spatial/Temporal resolution	Type of signal	Patient interaction	Level of personalization
MASK-air	Patient-reported symptoms and environmental/pollen data (CAMS, EAN, meteorology)	User level (via geolocation, daily logging)	Personalized symptom forecasts and alerts for high-risk days	Mobile app with daily symptom diary and notifications	High (individual symptom-based adaptation)
PollenSense	Local AI-powered sensor detecting airborne particles (pollen, spores)	Point location, near real-time	Real-time alerts with species-level discrimination	Mobile app linked to sensor device	Very high (personalized to the user’s immediate environment)
BreezoMeter	CAMS, local monitoring stations, traffic, satellite, and meteorology	High local resolution (street to city level), hourly updates	Multi-species pollen risk index and health recommendations	Mobile app + API for integration	Medium to high (location-based; limited direct symptom integration)
Airly	Dense network of low-cost sensors + meteorological/satellite data	Street/city scale, hourly updates	Air quality and pollen alerts	Mobile app, web dashboard, API	Medium (location-based, limited individual adaptation)
Tomorrow.io	Weather models + environmental data + ML	City/regional forecasts, hourly/daily	Hyperlocal pollen and weather-related risk alerts	Mobile app and API (for health providers and enterprises)	Medium (location-based personalization)
CAMS	European-scale atmospheric models + ground observations	Approximately 10-km grid, forecasts up to 3–4 days ahead	Regional pollen forecasts (general risk levels)	Via third-party apps (e.g., PASYFO)	Low to medium (region-based, indirect personalization via downstream apps)
Airmine	Satellite imagery (Sentinel-2), meteorology, local sensors, and user symptom diaries	Local maps, high resolution, daily	Pollen risk maps and push notifications	Mobile app with personal symptom tracking	High (adapted to individual symptom history)
PASYFO	CAMS pollen forecasts and personal symptom data	Local area, daily forecasts	Personalized allergy symptom forecasts	Mobile app with an interactive diary	High (individualized forecasts based on personal symptom data)
APOLLO	Electronic pollen monitoring network and self-reported symptoms of allergic rhinitis and asthma	Local area, up to a 60-day period	Indices visualizing individual symptom burden and daily pollen concentrations	Mobile app with an interactive diary	High (individual-level symptom and exposure data)

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CAMS: Copernicus Atmosphere Monitoring Service; EAN: European Aeroallergen Network; ML: machine learning; API: application programming interface; PASYFO: Personal Allergy Symptom Forecasting.

Declarations

Author contributions

JG: Conceptualization, Methodology, Supervision, Validation. J Corriger, MT, PA, J Charpy, JB, NV, and IAM: Investigation, Data curation, Writing—original draft. J Charpy, J Corriger, JG, PA, MT, YC, NDM, and JTB: Writing—review & editing. All authors read and approved the submitted version.

Conflicts of interest

JG reports speaker and travel support in the past 5 years from ALK, Stallergenes-Greer, Thermo Fisher Scientific, and Menarini outside the submitted work. J Corriger reports speaker and travel support in the past 5 years from ALK, Stallergenes-Greer, Thermo Fisher Scientific, and Menarini outside the submitted work. NV leads ongoing research projects involving Stallergenes-Greer and Pollensense. NV received remuneration from ALK, Stallergenes Greer, and Sanofi for expert services, conferences, or participation in symposiums. The other authors declare that they have no conflicts of interest relevant to this work.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publication

Not applicable.

Availability of data and materials

Not applicable.

Funding

Not applicable.

Copyright

Publisher’s note

Open Exploration maintains a neutral stance on jurisdictional claims in published institutional affiliations and maps. All opinions expressed in this article are the personal views of the author(s) and do not represent the stance of the editorial team or the publisher.

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