Over the past decades, epidemiological data have consistently painted a picture of an escalating prevalence of pollen allergies worldwide, including rhinitis and asthma [1–5]. Globally, allergic rhinitis now affects an estimated 20–30% of the population, with projections from organizations such as the World Health Organization suggesting that over half of the world’s population could be impacted by some form of allergy by 2050 [6], with allergic rhinitis ranking as the most common. In industrialized nations, prevalence rates have sometimes doubled or even tripled within a generation, with adolescents and young adults often exhibiting the highest rates. This rise is not limited to allergic rhinitis alone. Pollen allergy is a major risk factor for asthma, and the increasing rate of pollen sensitization is directly reflected in a growing burden of allergic asthma, particularly among children [7–11]. Moreover, beyond prevalence, many patients report worsening symptom severity and a longer duration of discomfort, turning what was once a seasonal nuisance into a chronic burden that significantly affects quality of life, as well as school and work productivity. The explanation for this resurgence lies in a set of interconnected mechanisms, forming a globally altered allergenic “exposome” that interacts with individual genetic and immunological susceptibility [12]. Taken together, these exposomic factors represent a significant threat, making pollen allergy likely to rise further.

While the early 20th-century “first wave” of the allergy epidemic was primarily fueled by agricultural shifts and improved public hygiene, climate change has undoubtedly emerged as the most powerful and well-documented driver of the rise in pollen allergies during the late 20th and early 21st centuries [13–15]. Its effects are multiple [12, 16, 17]. Climate change extends pollen seasons by enabling earlier springs and later autumns, significantly prolonging allergen exposure. Higher temperatures and increased atmospheric carbon dioxide concentrations boost plant growth, leading to increased pollen production. Furthermore, carbon dioxide and other pollutants can enhance pollen allergenicity by altering pollen biochemistry. Finally, warmer climates facilitate the expansion of allergenic plant ranges, introducing new allergens into previously unaffected regions and fostering new waves of sensitization.

Ambient air pollution significantly worsens both the development and severity of pollen allergies [17, 18]. Increasing evidence suggests that pollutants from traffic, industry, and domestic heating interact with pollen through several synergistic mechanisms. First, irritants such as fine particulate matter, nitrogen oxides, and ozone damage the respiratory epithelial barrier, making the airways more permeable and facilitating deeper penetration of allergens. Second, pollutants can alter the physical and chemical properties of pollen grains by adhering to their surface, increasing their fragility and promoting the release of allergenic proteins [19]. Finally, certain components of air pollution, especially diesel exhaust particles, exert an immunological adjuvant effect by activating immune cells and enhancing pro-allergic inflammation [20]. Together, these mechanisms lead to stronger and more severe allergic responses than exposure to pollen or pollutants alone.

The built environment also plays a significant role in increasing the pollen allergy burden [21, 22]. Urban planning has historically favored certain robust or low-maintenance tree species—such as birch, plane, or cypress—without considering their high allergenic potential. This limited botanical diversity in cities results in concentrated exposure to potent pollen sources. In addition, the urban heat island effect, whereby cities are warmer than surrounding rural areas, amplifies climate-driven increases in pollen production and release [23]. Beyond these environmental factors, shifts in human lifestyle—the so-called “personal exposome”—may further contribute to the rise in pollen allergy by disrupting the airway microbiome and modulating the immune system [24].

In an era defined by escalating pollen allergies and asthma, accurate and timely pollen monitoring becomes an indispensable tool for proactive health management, addressing both patients’ and allergists’ needs. It empowers individuals by providing actionable information and enables medical professionals to deliver more targeted care. For patients, timely information on pollen counts and forecasts is crucial for anticipating symptoms, allowing them to adapt outdoor activities and optimize medication initiation before symptom onset, thereby improving quality of life and reducing anxiety. For allergists, such data are essential for accurate diagnosis, enabling correlations between symptoms and specific pollen types, the tailoring of personalized treatment plans [including allergen immunotherapy (AIT) and medication adjustments], and more precise patient counseling. Ultimately, continuous and accessible pollen monitoring empowers both groups to navigate the increasing prevalence and severity of allergic diseases more effectively, fostering prevention, informed decision-making, and better health outcomes. Reliable pollen monitoring transforms an unpredictable environmental threat into manageable information, supporting proactive self-management for patients and enabling precision medicine for allergologists, in a landscape where allergic diseases are increasingly prevalent and aggressive.

In this context, artificial intelligence (AI) can play a transformative role in addressing the complex challenge of rising pollen allergies and pollen-induced allergic asthma by improving monitoring, prediction, understanding, and management through the integration of massive amounts of high-dimensional data. AI applications span data analysis, predictive modelling, personalized medicine, and even urban planning. This article aims to provide a clear and accessible overview of the current landscape of AI applications in pollen modelling. It will present practical tools such as pollen forecasts, collectors, sensors, and smartphone apps, and illustrate how allergists may use these to improve patient monitoring. The purpose of this article is to equip allergologists with the knowledge needed to assess and adopt these tools, moving toward a more anticipatory and personalized approach to pollen allergy management.

The European Environment Agency defines pollination as “the transfer of pollen from an anther to a stigma in angiosperms, or from a microsporangium to a micropyle in gymnosperms” [25]. This process occurs only in spermatophytes, which include angiosperms (flowering plants) and gymnosperms (seed plants) [26]. The first step in pollen release is anthesis, defined as the period during which a flower is fully open and functional [27].

The mechanisms of anthesis are classified as either active or passive, depending on whether they are self-powered or require energy input from the environment. Active pollen release mechanisms involve programmed cell death and turgor changes in anther tissues that control the timing of dehiscence. Vibrational or mechanical stimuli from pollinators can trigger pollen ejection, enabling precise synchronization between floral function and pollinator behavior. In principle, active release can occur independently of wind speed and turbulence intensity because the mobilizing force is generated internally. However, active release is sometimes triggered by environmental cues indicating favorable conditions for wind dispersal [28]. Pollen release can also occur through passive biomechanical mechanisms, driven by tissue dehydration and hygroscopic movement within the anther wall. This allows dehiscence to occur without active cellular control. These passive processes are highly sensitive to meteorological factors: temperature and relative humidity regulate anther drying dynamics, while wind intensity and rainfall influence the timing and efficiency of pollen liberation and dispersal. Exposure to wind can lead to pollen removal when the forces experienced by pollen grains exceed the resistive forces keeping them attached to the anthers [29].

Meteorological measurements (e.g., relative humidity, solar radiation, temperature, and wind speed) and pollen concentration data indicate a causal relationship; however, determining the relative effects of individual meteorological variables is challenging given their frequent temporal correlation [30]. Environmental conditions favoring wind pollination have been inferred mainly from biogeographic and habitat trends in the occurrence of wind-pollinated species [15]. Habitat complexity and persistent rainfall are generally thought to disfavor wind pollination because both can result in pollen loss from airflows [31].

Both conventional methods and new technologies are used to assess and forecast pollen. Conventional approaches rely on aerobiology and involve capturing airborne pollen, followed by manual identification and quantification. The volumetric trap (e.g., Hirst-type) is considered the gold standard, drawing a known volume of air onto an adhesive surface over time [32]. This surface is then analyzed microscopically by trained personnel to count and identify different pollen types, yielding quantitative data expressed as pollen grains per cubic meter. The Hirst trap operates on the principle of volumetric impact: air is aspirated at a constant flow rate of 10 L/min, accelerating particles that are then projected onto an adhesive surface. A rotating drum equipped with a mechanical clock allows continuous sampling for 7 days without intervention, with a temporal resolution of approximately 2 hours. After this sampling period, the tape is prepared by adding a stain and cutting it into daily slides. Analysis is performed under a microscope by analysts specifically trained in pollen recognition. These analysts are a key asset of the method because they can routinely identify approximately 100 pollen taxa and up to 20 fungal spores. Although it is technically possible to conduct 24-hour sampling, the substantial additional labor required means this option is almost never used by measurement network managers.

The collection efficiency of Hirst traps can be influenced by wind speed and is highly sensitive to accurate mechanical calibration of the aspiration flow rate [33]. The margin of error for sampling with such sensors is estimated to be on the order of 20–30% [34]. Despite these limitations, Hirst traps are widely used, with more than 500 stations worldwide (https://pollenscience.eu/) [35], and some sites provide sampling records spanning several decades. Their broad adoption is largely attributable to relatively low equipment costs (approximately €5,000 per sampler), mechanical robustness, and standardized operating procedures [36]. These traps provide continuous and comparable daily records, along with detailed morphological identification of pollen taxa. However, their operation is labor-intensive, time-consuming (with a 24- to 48-h delay), and costly because of the need for highly specialized personnel. The principal drawback of aerobiological monitoring remains the temporal latency between the start of sampling and the availability of analytical results, which typically occurs 7–8 days later. Historical datasets of pollen and mold measurements collected by the Réseau National de Surveillance Aérobiologique (RNSA) between 1987 and 2024 in France, as well as historical sensor data from across Europe, are available in open access [35, 37].

Pollen assessment through modeling provides broader spatial coverage and forecasting capabilities, complementing physical sampling techniques. These models integrate diverse data sources, with satellite imagery playing a central role in mapping vegetation and monitoring phenology. Satellite data are particularly valuable for characterizing the spatial distribution of allergenic vegetation—thereby improving source term estimates—and for tracking large-scale plant growth cycles, which are essential inputs for both phenological and dispersion models (see Current AI-based tools and platforms in pollen forecasting and patient monitoring).

AI can be defined as the ability of a machine to reproduce human-like behaviors, such as reasoning, planning, and creativity [38]. AI encompasses several branches, among which ML and deep learning (DL) are particularly relevant for predictive modelling.

ML algorithms learn statistical relationships from data in order to perform prediction or classification tasks. Rather than being explicitly programmed, these models identify and learn patterns either from labeled examples (supervised learning) or by autonomously discovering structure in unlabeled data (unsupervised learning) [39, 40]. Common ML techniques include linear and regularized regression models, tree-based methods such as random forests or gradient boosting, support vector machines, and ensemble approaches that combine multiple algorithms to improve accuracy. These methods are especially effective when large, well-curated datasets are available and when relationships between variables remain relatively stable over time.

DL is a specialized subset of ML based on artificial neural networks composed of multiple computational layers. Inspired by the architecture of the human brain, these networks are particularly effective at capturing complex and nonlinear interactions in large datasets [41, 42]. DL is especially relevant for environmental exposure prediction because many key inputs—such as meteorological signals, pollen measurements, and satellite data—are time series or spatially structured. Recurrent neural networks are designed to learn temporal patterns, including daily and seasonal pollen cycles or sudden bursts following thunderstorms [41, 43]. Convolutional neural networks can extract meaningful information from satellite imagery or land use maps [44, 45]. DL models often include millions of parameters that are iteratively optimized during training, allowing them to ingest heterogeneous information (weather, vegetation indices, pollution, sensor data) and fuse it into a coherent predictive signal [41]. Owing to their performance, these models are increasingly used in modern pollen forecasting systems, particularly when high-resolution and short-term predictions (< 72 hours) are required. However, their accuracy depends on the availability of large, high-quality datasets, and their training demands substantial computational resources [41, 44, 45].

In contrast to traditional statistical approaches, AI systems can incorporate a wider variety of information and automatically learn from data, improving over time. These models typically ingest multiple sources of input, including meteorological data (e.g., temperature, humidity, wind speed, rainfall), phenology, land use and vegetation indices (e.g., satellite imagery, land cover), historical pollen measurements (e.g., time series and long-term trends), environmental co-factors (e.g., air pollution), and clinical data from patients (e.g., symptom trackers, clinical scores, mobile health data) [44, 46].

By processing large volumes of information (big data) and identifying interactions that would be impractical to model manually, AI systems can capture the complex, nonlinear dynamics of pollen emission and dispersion. Their predictions can then be integrated into decision-support tools, whose outputs must be clinically interpreted before being applied in practice.

By integrating heterogeneous data sources—such as meteorological variables, satellite-derived phenology, air pollution levels, symptom diaries, and real-time sensor measurements—into a coherent predictive signal, AI models using multimodal approaches can generate forecasts that are more closely aligned with actual exposure and its clinical consequences (Figure 1). When combined with individual symptom patterns, AI can also support personalized allergy forecasting, enable the development of risk scores, and inform patient-specific clinical decision-making to tailor preventive and therapeutic strategies, including preventive recommendations, treatment optimization, seasonal AIT, and avoidance measures. These data-driven approaches represent a paradigm shift from population-based pollen calendars toward individualized exposure assessment in modern allergology and precision medicine [47, 48].

AI-enabled pollen services become clinically meaningful only when they are linked to symptoms, treatments, and decisions at the individual patient level. For allergologists, the key question is no longer how accurate a city-level forecast may be, but rather how this information can be integrated into consultations to improve the management of rhinitis and asthma. AI can be used to generate pollen forecasts, feed automated alert systems, integrate patient feedback (e.g., symptoms and behavior) to refine iterative models, and support clinical decision-making in routine practice.

While tools based on AI techniques offer substantial promise for pollen forecasting and patient alert systems—benefiting allergists, patients, and public health more broadly [5, 69, 70]—their widespread adoption in clinical practice remains constrained by several important challenges.

A first barrier relates to trust and clinical reasoning when decisions are supported by automated systems. Many clinicians perceive AI tools as complex or insufficiently transparent, particularly when forecasts or alerts are generated by “black-box” models that offer little insight into why specific predictions or recommendations are produced. Limited training in AI literacy and data science, together with the absence of standardized evaluation frameworks, may further amplify this hesitancy [63]. Another concern is the potential dehumanization of the doctor–patient relationship, along with the risk of “de-skilling” when routine cognitive tasks are delegated to AI systems [70]. AI-based tools should therefore be regarded as complementary: clinicians are encouraged to maintain a patient-centered approach and to use these systems to enhance—rather than replace—their clinical expertise [69, 71].

Data heterogeneity represents an additional major obstacle. Pollen monitoring networks, environmental and geographical factors, agricultural practices (e.g., irrigation), allergen panels, and classification methods vary widely across countries and regions. These discrepancies lead to inconsistent input data, which may compromise model performance, hinder cross-regional comparability, and limit generalizability [47, 71, 72]. Furthermore, population-representation biases persist: many AI models are developed primarily using adult datasets, reducing the reliability of predictions when applied to children or adolescents [69, 72].

Beyond cognitive, contextual, and adoption-related issues, the implementation of AI-enabled systems raises critical questions regarding privacy, ethics, and cybersecurity. Many applications rely on sensitive patient data—including personal information, geolocation, symptom and medication diaries, and detailed environmental exposure histories—to generate personalized alerts [47, 70, 71, 73–77]. If not properly secured or anonymized, such data pose clear privacy risks. Robust privacy-preserving strategies—including de-identification protocols, encrypted data storage, and differentiated access rights—are therefore essential. Synthetic data generation offers a particularly promising avenue for protecting individual privacy while still supporting model training and validation [71]. In parallel, adherence to regulatory frameworks such as the General Data Protection Regulation remains crucial to ensure responsible data governance. In addition, AI-based forecasting systems may underperform in heterogeneous populations and produce biased outputs related to gender or ethnicity, thereby potentially exacerbating inequities in healthcare delivery [69, 70, 73, 75].

To date, AI-based forecasting and patient alert tools have been evaluated predominantly by engineering and technical experts. Assessments have focused mainly on technological maturity and feasibility [47, 73, 78], forecasting accuracy [47, 57, 79, 80], predictive performance [76], and content-related features such as mapping, risk scale design, and system-generated health recommendations [81]. However, these metrics do not necessarily reflect clinical relevance or real-world benefit for patients and allergists.

Fostering synergy between technological innovation and clinical practice is therefore essential [69]. From a clinical perspective, a rigorous and critical appraisal of AI-based pollen tools is indispensable to ensure human-centered care [74]. Key evaluation criteria include clinical purpose and effectiveness, data quality, model transparency and explainability, ethical and security safeguards, and user-centered design and usability [71, 73, 75, 82]. Randomized trials, prospective studies, and real-world observational research—combined with systematic patient feedback—remain the gold standard for determining clinical utility [71, 77, 83]. Insights from everyday users, both patients and clinicians, may be particularly valuable for understanding adherence, perceived benefits, and real-world usability.

The future of pollen allergy management lies in precision forecasting and the seamless integration of technology into clinical practice. Precision forecasting will increasingly rely on the incorporation of individual immunological data and patient phenotypes into predictive models, supported by adaptive AI systems capable of learning from each patient’s unique patterns and responses. This evolution is likely to extend to “embedded AI” within smart medical devices such as inhalers and wearables, delivering highly personalized, real-time insights in support of precision medicine (Table 2).

Bridging the gap between technological innovation and real-world clinical practice will require strengthened collaboration among allergists, immunologists, engineers, data scientists, and epidemiologists, along with continuous training to ensure responsible and informed use of these tools. AI enables a proactive and personalized approach to pollen allergy management, and many of the necessary tools already exist. Their clinical adoption is now a key step to promote uptake by patients and to enable subsequent evaluation and refinement within a virtuous cycle of medical and scientific assessment [84–86]. Allergists are well-positioned to play a central role in guiding this technological transition, ensuring that AI-based solutions remain clinically relevant and useful, ethically aligned, and firmly centered on patient needs.