Leveraging Artificial Intelligence in Allergy, Asthma, and Immunology With Environmental Exposures

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.

1. Introduction

The integration of Artificial Intelligence (AI) into environmental health sciences (EHS) is increasingly prevalent, offering novel methodologies for analyzing complex datasets and addressing public health challenges, including chronic and complex diseases such as allergies and asthma. AI and its subfields, such as Machine Learning (ML) and Deep Learning (DL), are commonly used in EHS. Others include Natural Language Processing or causal AI. ML employs statistical modeling and optimization techniques to enable systems to infer patterns and generalize from empirical data without explicit rule‐based programming. Deep Learning (DL) is a subset of ML that uses multi‐layered “deep” neural networks where each layer processes information and passes it to the next to identify intricate features, making it capable of automatically learning new features from raw data [1].

Here, we discuss how AI is advancing the assessment of complex exposures associated with the exposome, identifying emerging toxic substances, strengthening evidence for exposure–disease causality, and improving our understanding of complex diseases. Additionally, AI supports climate disaster forecasting, guides targeted interventions, and enables scalable mitigation strategies. We also discuss the limitations and challenges of AI in EHS.

1.1. AI, the Exposome, and Assessment of Complex Exposures

Traditionally, environmental health research has focused on a single identifiable exposure; however, this is inadequate as it fails to account for complex, real‐world synergistic effects and cumulative impacts of multiple exposures. These multiple exposures that affect human health are termed the exposome, which is defined as the totality of environmental exposures (chemical, physical, biological, and psychosocial) a person experiences from conception to death, complementing the genome to determine health. To understand the role of the exposome on human health, researchers are turning to AI. The Human Exposome Project endeavors to systematically characterize the lifetime effects of environmental exposures on human health [2]. Leveraging AI and advanced analytical technologies, this approach facilitates the identification of previously unknown exposure‐health relationships and the development of personalized prevention strategies. The project signifies a paradigm shift toward exposure‐centric methods for understanding and mitigating chronic diseases.

AI has been used to analyze large datasets integrating occupational exposures, biomarkers, and health information and can help elucidate the complex relationships between the work environment and disease [3] (Figure 1). Causal inference methods applied to exposome research enable building predictive models that estimate the health effects or molecular changes resulting from specific occupational exposures or their combinations [4]. For example, AI analysis of occupational exposome data may identify how cumulative exposure to certain workplace chemicals, combined with other factors like stress, affect disease risks or induce epigenetic alterations. Moreover, ML models incorporating personal air pollution exposures, physical activity levels from mobile phones, and clinical biomarkers could forecast asthma risk and guide tailored interventions. Causal inference methods like Mendelian randomization (which uses genetic variants as instruments to infer causal relationships between modifiable exposures and health outcomes), coupled with ML, can more convincingly establish environmental contributions to disease. A study extracted 8 single nucleotide polymorphisms (SNPs) associated with PM_2.5, 22 SNPs associated with PM₁₀, 7 SNPs associated with NO₂ and 8 SNPs associated with NO_x as genetic variants on European ancestry. The study found that PM₁₀ is causally associated with Alzheimer's disease risk. Genetically PM_2.5 and NO_x exposure showed a significant association with cognitive decline in people of European origin [5].

FIGURE 1.

Applications of AI in exposome and personalized medicine.

The risk assessment of chemicals is a critical component of Environmental Health. In the field of toxicology, AI plays a crucial role in revolutionizing the assessment of chemical safety by analyzing diverse and extensive datasets to predict toxicological effects [6]. AI is transforming toxicology from an empirical, animal‐based science into a data‐rich, predictive, and mechanistic discipline. It can predict toxicity directly from molecular structure, allowing prescreening of tens of thousands of untested industrial chemicals [7]. Additionally, ML is being utilized to anticipate toxicity within complex biological systems, tackling issues related to data accuracy and the clarity of model results [8]. ML models trained on large toxicological datasets can accurately predict the toxicity of new chemicals, reducing the need for animal testing [9]. DL enables the integration of chemical structures, in vitro assays, and ~ omics data to provide a more comprehensive understanding of toxicity mechanisms [6]. Natural language processing allows automated extraction of knowledge from scientific literature and legacy toxicity reports, boosting data retrieval. AI also facilitates evidence integration across diverse data streams for quantitative, probabilistic risk assessment [10]. Explainable AI techniques are increasing the interpretability and transparency of these models. Overall, AI is accelerating the pace of chemical safety assessment, enhancing the identification of toxicity pathways, and enabling a transition toward animal‐free, evidence‐based toxicology.

2. Application of AI for Complex Chronic Diseases Such as Allergies and Asthma

Allergies and asthma are multifactorial diseases and serve as well‐characterized examples demonstrating the relationship between environmental exposures and disease prevalence or management. Epidemiologic studies have linked exposure to pollutants with increased prevalence of atopic dermatitis. For instance, ML analyses have identified that exposure to diisocyanates, chemicals used in polyurethane production, is associated with microbial dysbiosis and atopic dermatitis [11]. Similarly, AD flares have been associated with wildfires [12]. Using ML. a study identified a set of 35 genes and 50 microbiota features that are predictive for AD. Of these, at least three genes and three microorganisms were directly or indirectly associated with AD [13]. A study used interpretable ML for allergic rhinitis prediction among preschool children in China. The study analyzed questionnaire data from 7131 children aged 2–8, which was randomly divided into training, validation, and testing sets. Predictor variables included parental allergy, medical history during the child's first year, and early life environmental factors. The analysis identified the five most predictive variables for allergic rhinitis as history of allergic rhinitis in the mother or father, having older siblings, a history of food allergy, and paternal educational level [14]. A Taiwanese cohort study conducted a 14‐year follow‐up of infants born to pregnant women between 2000 and 2005. Data were collected via questionnaires covering demographics, socioeconomic status, lifestyle, medical history, and 24‐h dietary recall. Hourly concentrations of air pollutants within the year prior to delivery were obtained from 76 national air quality monitoring stations. Employing ML techniques, the study demonstrated that prenatal exposure to nitrogen dioxide and its temporal changes were significant predictors of atopic dermatitis and allergic rhinitis through adolescence [15].

Pollen can be a trigger of allergic rhinitis or asthma symptoms. Temperature and carbon dioxide levels have been associated with longer pollen seasons and with increased pollen concentrations and allergenicity [16]. Exposure to pollution has been associated with adverse impact on asthma and chronic obstructive pulmonary disease (COPD) [17, 18]. It is therefore expected that climate change will have relevant consequences for patients with respiratory diseases. In this context, AI algorithms have been used not only for projections on environmental variables (including pollutant and pollen levels) but also to assess the association between environmental factors and chronic respiratory diseases and to develop models which can predict exacerbations or health services use (Figure 2).

FIGURE 2.

ML and DL in predicting risk of allergy and asthma exacerbations and health service use.

Several studies have used AI to clarify the association between pollutants and respiratory diseases [19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]. For example, AI algorithms (including extremely randomized trees and random forests) have been used to estimate the concentrations of pollutants with high spatial resolution [19, 20, 21, 22, 23, 24, 25, 26], shedding light (i) on how PM₁ is more strongly associated with the risk of childhood asthma compared to PM of other sizes [19], (ii) on how PM_2.5 and ozone levels contribute to asthma symptoms and respiratory health services use both individually and jointly (with PM_2.5 appearing to display a more relevant impact in adjusted models) [20, 24], (iii) on the association between PM_2.5 levels and emergency department visits due to asthma or other respiratory conditions [23, 24], (iv) and on the deleterious impact of PM_2.5 and PM₁₀ levels in rhinitis and COPD [21]. ML‐based algorithms have also enabled (i) the identification of combinations of toxics associated with asthma symptoms and health services use [27, 28], (ii) the differential assessment of the association between exposure to volatile organic compounds and respiratory emergency department visits [29], and (ii) the finding that exposure to trees is associated with a protective effect on asthma (with the inverse effect being observed for grass exposure) [30]. Performance of these studies would have not been possible with traditional statistical methods (e.g., distinguishing vegetation types required the use of a DL model segmenting street view images [30]; identification of toxics whose effects on asthma are only exerted in combination required ML algorithms [27, 28]).

Random forest models and other ML algorithms have also been used in models predicting the development of chronic respiratory diseases or their symptoms based on environmental, clinical and demographic data [15, 31, 32, 33]. These studies have contributed to epidemiological advances, including by describing the associations between (i) prenatal exposure to nitrogen dioxide and childhood rhinitis [15], (ii) home‐ and school‐based exposures and asthma symptoms [32, 33], and (iii) family history of allergic rhinitis and asthma [32].

In the context of predictive models, neural networks have been used to forecast hospitalizations and emergency department visits due to chronic respiratory diseases (mostly asthma) [34, 35, 36, 37, 38, 39], with the first studies dating back to the 1990s [34]. The models considered different sets of input variables, including data on air pollution [35, 36, 37, 38, 39], meteorological conditions [35, 36, 37, 38], pollen levels [35], cases of influenza [35] or even infodemiology data (Google Trends or tweets) [39]. A Korean study using meteorological, air pollution, and viral data developed a model to predict asthma exacerbations on a daily basis [40]. ML applied to large‐scale real‐world data from 13,498 patients was able to identify 5 clusters of patients with asthma with distinct clinical features which may assist with individualized management strategies [41]. Several of these models achieved good performance, as measured by their accuracy [37] or strong correlation between predicted and observed hospitalizations in testing datasets [35, 36]. Results of these studies can have relevant implications for health services management as may those which used random forest algorithms to assess the association between air pollutant levels and asthma medication use [42, 43]. In one of those studies, dispensation of rescue medication was assessed during the wildfire season [42] while in the other study, digital sensors were attached to asthma inhalers, so that researchers could track the location of their use and its association with increased exposure to pollutants [43]. Further implications of these models lie in the possibility of developing early warning systems that can be directly available to patients through mobile health applications [16]—patients could be warned in advance when their symptoms would be expected to worsen and take adequate preventative measures. Asthma is a heterogeneous disease caused by genetics as well as environmental factors. The two main endotypes associated with asthma are Th2 high or Th2 low. One application of ML would be helping to unravel true asthma endotypes with input from clinical features, genetic and omics data, paving the way for personalized medicine [44, 45, 46].

3. AI in Predicting Climate‐Related Disasters and Modeling Effective Interventions

In the face of escalating climate‐related disasters, the importance of prompt and effective responses to mitigate the significant risks of wildfires, hurricanes, floods, heatwaves, and other natural disasters on human health, ecosystems, and global economies is paramount. The integration of AI into disaster management strategies presents a transformative approach to enhancing the resilience of societies to exacerbating climate phenomena. The ability of AI systems to provide real‐time analyses and predictions is crucial for preempting disaster impacts, optimizing resource allocation, and facilitating efficient recovery efforts.

Recent advancements in AI have ushered in a new era of environmental monitoring and disaster prediction. ML has been used to estimate population‐wide levels of drinking water contaminants such as arsenic or lead based on limited exposure data [47, 48, 49]. Similarly, ML models, for example, have demonstrated remarkable success in forecasting ambient air pollution levels by training models on existing exposure data to generate predictions for areas and populations lacking such data, a significant concern during wildfires and industrial accidents. For example, ML has been used to predict PM_2.5 values stemming from various causes of air pollution in areas lacking sensors, validated on areas with sensors [50, 51, 52]. Gu et al. [53] and Palanichamy et al. [54] have explored the use of AI in predicting particulate matter concentrations (PM_2.5), highlighting AI's potential in issuing timely warnings to mitigate health risks. Similarly, studies reported by Razavi‐Termeh et al. [55] and Bhowmik et al. [56] apply ML techniques to model environmental and meteorological effects on asthma‐prone populations, showcasing AI's capabilities in identifying vulnerable individuals and facilitating targeted interventions. The predictive power of AI extends beyond air quality. Studies like those by Bekkar et al. [57] demonstrate the efficacy of DL algorithms in forecasting air‐pollution levels in smart cities, emphasizing the potential applications of AI in enhancing urban resilience against environmental hazards. Additionally, Bhowmik et al. [58] created a spatio‐temporal neural network to successfully predict large wildfires up to 2 weeks in advance, demonstrating the feasibility of AI in forecasting and potentially mitigating imminent natural disasters (Figure 3). This predictive capacity is pivotal not only for immediate disaster response but also for long‐term planning and mitigation strategies. By forecasting the onset and severity of climate‐related events, AI can enable policymakers and disaster management teams to deploy resources more strategically, thereby minimizing the adverse impacts of such events on communities.

FIGURE 3.

Different applications of AI in exposure assessment and potential health impact prediction.

ML can be used in tandem with early warning systems to facilitate anticipatory action, where resources are deployed to communities at risk of being afflicted by an impending disaster [59]. These methodologies have been effectively implemented to deliver anticipatory cash transfers, facilitate evacuations, administer first aid, and distribute food supplies to households considered at risk from imminent hazards such as floods, tropical cyclones, droughts, and cold waves [60, 61, 62].

However, despite these promising developments, the application of AI in responding to climate‐related disasters is not without its challenges. The accuracy and reliability of AI predictions heavily depend on the quality and quantity of data available. As highlighted by Singh et al. [63] in their review of emerging technologies for inhalation toxicology, integrating diverse data sources—from satellite imagery to ground‐based monitoring stations—can enhance model robustness. The work by Bhowmik et al. [58] also shows how wildfire prediction accuracy increased substantially with the incorporation of meteorological data such as humidity, temperature, and wind speed in addition to environmental data such as particulate counts and noxious gas levels. This calls for a concerted effort to improve data collection and sharing mechanisms across different stakeholders.

Furthermore, the development of contemporary AI models has identified domains requiring enhancement. Variability in performance across diverse geographic regions and disaster categories underscores the necessity for localized models tailored to specific environmental and socio‐economic factors. Studies such as that by Neo et al. [64] on integrated air pollution monitoring underscore the importance of federated learning approaches in tailoring predictions to local conditions while preserving data privacy. In addition, there is a growing recognition of the need to make AI models more interpretable to users, including policymakers, emergency responders, and the general public. The work by Li et al. [65], which develops interpretable ML models for heavy metal exposure, points toward a broader trend of enhancing model transparency to foster trust and facilitate the implementation of AI‐driven recommendations.

In summary, the deployment of AI in responding to climate‐related disasters offers significant potential to improve the agility and efficacy of disaster management. By utilizing real‐time data and predictive analytics, AI could transform the ways in which societies anticipate, respond to, and recover from such events. Future efforts should prioritize enhancing the accuracy, reliability, and interpretability of AI models, while promoting cross‐disciplinary collaboration to fully realize AI's capacity to protect human and environmental health amid an increasingly volatile climate.

4. AI in Optimizing Environmental Mitigation Strategies

The profound capacity of AI models for data processing and predictive analytics offers transformative potential in devising strategies to mitigate environmental damage, particularly in carbon capture and waste management domains. Through predictive modeling and ML algorithms, AI facilitates the identification of efficient and sustainable approaches to reduce carbon emissions and enhance waste recycling and reduction efforts.

In carbon capture, AI models have been instrumental in refining the selection of carbon sequestration materials and processes. Predictive models analyze various capture technologies and materials to ascertain their effectiveness under differing conditions [66, 67]. Moreover, AI‐driven optimization models aim to improve carbon capture plants' operational efficiency, significantly reducing the energy consumption and overall costs associated with carbon sequestration [68].

The waste management sector benefits from AI through the automation of waste sorting and recycling processes. ML algorithms, integrated with computer vision, enable precise identification and segregation of recyclable materials, thereby enhancing the efficiency and purity of recycling outputs [69, 70, 71]. Predictive analytics also play a crucial role in forecasting waste generation patterns, optimizing collection routes, and improving recycling processes [72, 73].

Several AI models currently in operation exhibit the effectiveness of AI in environmental mitigation. For instance, neural networks have been employed to predict the efficiency of carbon capture materials, providing insights into their performance under various environmental conditions [74]. In waste management, AI algorithms have been developed to predict waste generation trends, facilitating the optimization of waste collection and recycling processes [72, 73].

Despite the advancements, several areas require further development to fully harness AI's potential in environmental mitigation. Integration of AI with Internet of Things (IoT) technologies, which is defined as a network of physical devices, vehicles, appliances, and other items embedded with sensors, software, and other technologies that enable them to connect and exchange data with each other and systems over the internet, could revolutionize real‐time monitoring and control of environmental mitigation processes, offering adaptive solutions to dynamic environmental changes [75]. Additionally, the development of sophisticated AI algorithms capable of processing complex and heterogeneous environmental data is essential. These algorithms should be designed to learn from sparse and incomplete datasets, enhancing their adaptability and application in diverse environmental contexts [76]. Furthermore, ethical and transparent AI practices must be prioritized to ensure the sustainability and public acceptance of AI‐driven environmental mitigation strategies. Incorporating ethical considerations in AI model development and deployment can foster trust and cooperation among stakeholders, crucial for the successful implementation of environmental policies [77, 78].

The application of AI in environmental mitigation strategies, particularly in carbon capture and waste management, underscores its potential to significantly contribute to the global effort against environmental degradation and the green technological revolution in environmental research. By harnessing AI's capabilities in predictive modeling and data analytics, environmental mitigation strategies can be optimized for efficiency and sustainability. However, continuous advancements in AI technology and integration with IoT devices, alongside the development of sophisticated algorithms and ethical AI practices, are imperative for realizing the full potential of AI in environmental health and preventing natural disasters.

5. AI Applications for Estimating Environmental Health Risks and Personalized Medicine

Estimating health effects stemming from environmental factors presents multifaceted challenges. Accurately gauging chemical exposure poses a significant hurdle due to the variability in exposure sources, individual behaviors, and environmental conditions [79]. Furthermore, confounding factors add complexity by potentially influencing both exposure and disease outcomes, complicating the establishment of clear associations between environmental factors and health impacts [79]. Identifying periods of increased susceptibility to chemical exposures, especially during early development, and understanding the effects of chemical mixtures further complicate these challenges [79]. Additionally, the disparate measurement protocols employed in environmental health studies hinder data consolidation and comparison, crucial for meta‐analyses and comprehensive risk assessments [80]. Methodological complexities in comparative risk assessment, including defining minimal risk exposure levels and accounting for non‐additive population attributable fractions, complicate the estimation of the burden attributable to environmental risk factors [81]. Moreover, the ethical and economic considerations arising from the long‐term and uneven distribution of health impacts due to climate change add another layer of complexity to policy decision‐making [82]. While advancements in environmental exposure assessment have enhanced the precision of policy actions, there remains a need to better integrate health‐relevant exposures with economic, behavioral, biological, familial, and environmental variables [83]. Addressing these challenges is imperative for developing effective prevention policies and achieving public health objectives.

AI offers promising approaches for transforming environmental health research by addressing numerous prevailing challenges. These technologies are particularly adept at handling and analyzing extensive datasets characteristic of environmental health investigations, where complex interactions between pollutants and health outcomes are common [84]. AI's capacity to identify subtle patterns and associations beyond the reach of traditional statistical methods yields critical insights into the etiology of environmentally related diseases. ML algorithms enable the development of predictive models for outbreaks and contaminant dissemination, supporting proactive public health responses. Furthermore, AI facilitates personalized risk stratification by integrating individual factors such as genetics, lifestyle, and exposure history, thereby informing tailored health recommendations. In exposure assessment, AI enhances the precision and granularity of estimates through the integration of diverse data sources, including satellite imagery and personal monitoring devices, improving understanding of spatial and temporal variability in environmental exposures. These technologies also streamline the screening of chemicals for potential toxicity, an essential task given the vast number of uncharacterized compounds in the environment. By predicting toxicological endpoints, AI helps prioritize chemicals for further evaluation and regulatory review. Collectively, these advancements hold promise for transforming environmental health research by improving data analysis, predictive modeling, exposure assessment, and chemical screening, with the potential to strengthen public health protections [85]. Notably, Lei et al. have employed ML to make predictions of chronic toxicity for fresh water organisms using molecular information of pollutants [86]. This model can be useful for population health effects once paired with relevant public disease databases. More advanced models such as graph‐based DL have also been employed to predict in silico human organ toxicity to different chemical molecules which can advance both drug development and environmental toxicology management [87]. In the field of air pollution, deep neural networks have been used to predict the rate of asthma exacerbation based on air pollution, weather, pollen, and influenza [35].

In the realm of precision medicine, AI has been increasingly utilized for both drug design and prediction of clinical outcomes [88]. AI models are capable of handling complex data structures of each patient based on their demographic, genomic, and proteomic data to inform the most effective personalized treatment strategies. For example, AI applications have been used for therapeutic drug monitoring and model‐informed precision dosing to ensure accurate dosing for each patient [89]. In oncology, AI is also used to predict drug response based on genomic and biomarker data, which can help to guide treatment decisions and stratification in clinical trials [90, 91, 92]. Furthermore, AI has also played a big role in large‐scale omics and clinical data analysis for patient disease outcome stratification with disease outbreaks like in the context of COVID‐19 [93]. By mid‐2024, over 1000 AI‐enabled medical devices have been FDA approved, but none specifically addressed allergic diseases [94]. However, future models that combine not only patients' demographic and omics data but also surrounding environmental exposures are still needed for a complete comprehensive precision medicine model.

6. AI and Environmental Justice

AI can contribute to environmental justice by bridging data gaps (Table 1 and Figure 4) and ascertaining there is adequate data from marginalized populations. Previously mentioned approaches like transfer learning, post‐stratification, downscaling, and data fusion with modalities like satellite imagery can all help bridge data gaps by providing estimates of both environmental exposures and outcomes. Similarly, approaches like ML‐enabled heterogeneous exposure effect estimation can characterize how marginalized populations or subgroups are differentially impacted by environmental exposures [95, 96].

TABLE 1.

Challenges of AI applications in environmental health sciences (EHS) and possible solutions.

Challenges in applying AI in EHS	Proposed solutions
Data collection bias
Socioeconomic bias: Certain under‐represented populations or regions are excluded from the training dataset.	Collaborate with local agencies and community groups to gather data that accurately reflects diverse populations and regions involved.
Temporal bias: Data is collected during a specific period that might not be representative of historical or future environment.	Implement year‐round or multi‐year data collection efforts to capture temporal variations and trends.
Instrumental bias: Variation in collection instruments can lead to inconsistency in the data set.	Standardize collection methods and instruments across all data collection points to ensure consistency.
Algorithm bias.
Assumption bias: Assuming certain relationship in the data like linear relationship for non‐linear data.	Use flexible models that can capture non‐linear relationships in the dataset.
Optimization Bias: Metrics selected for optimization do not align with the broader goal of fairness or equity.	Employ multi‐objective optimization processes to balance performance, accuracy, and fairness.
Feature Engineering Bias: Experts select biased features for the model, or initial features become outdated.	Regularly review and update selected features, incorporating equity and fairness into the model design goals.
Lacking oversight bias
Lack of Oversight Committees: No communication between domain experts and engineers who train the model.	Establish oversight committees involving both domain experts and engineers to ensure fairness and accuracy throughout model development.
Lack of Institutional and Government Policies: Absence of oversight policies at institutional and government levels.	Implement comprehensive oversight policies at governmental and institutional levels to ensure fairness and safety in AI models.

FIGURE 4.

Bias in data collection, algorithm design, or lack of oversight policy can lead to ineffective AI models in public policy.

Environmental regulatory surveillance can be improved by AI methods, particularly in low‐resource areas where manual surveillance may be too costly to conduct at frequent intervals. For example, ML has been used to predict water quality violations before they occur, serving as an early warning system [97, 98, 99]. ML has also been used in conjunction with satellite imagery to map out concentrated animal feeding operations [100, 101], harmful algal blooms [102, 103], and fertilizer runoff associated with schistosomiasis [104].

Similarly, applications of AI in environmental health policy or resource allocation may be influenced by algorithmic bias. Biases rooted in datasets, model architecture, and the intentions of developers can introduce subjective preferences into algorithmic decision‐making, potentially creating an illusion of objectivity [105, 106]. For example, if an AI model is trained primarily on data from urban areas, it may fail to accurately predict flood risks in rural or low‐income areas that lack similar infrastructure, potentially leading to a lack of timely warnings resulting in disproportionate damage and loss during flood events [107].

Ultimately, advances in AI can promote environmental justice only when affected communities are actively engaged in the development of AI applications. Principles of environmental justice encompass both distributive and procedural justice, with the former referring to the equitable allocation of resources and protection from environmental harms, and the latter ensuring fair representation in decision‐making processes [108, 109]. Most applications of AI for environmental justice focus on distributive justice, but an emphasis on procedural justice is required to address critical issues such as extractive data practices, surveillance, allocative harm, and discriminatory enforcement [110].

7. Methodological Challenges and Advances

Several methodological challenges unique to environmental health are increasingly being addressed through advancements in AI. Notably, progress in estimating heterogeneous treatment effects in high‐dimensional contexts now allows for improved assessment of how environmental exposures impact health across different subpopulations [95, 96]. Secondly, comparable ML techniques can be employed to enhance the accuracy of modeling nonlinear exposure‐response relationships in high‐dimensional contexts [111, 112], a crucial task for informing environmental health policy [113]. Lastly, ML can be used to estimate the effects of multi‐exposure mixtures through approaches such as Bayesian Kernel Machine Regression or Bayesian profile regression, as environmental exposures typically do not occur in isolation [114, 115].

The integration of Bayesian ML into environmental public health provides a rigorous framework for quantifying uncertainty—a critical requirement when associating complex health outcomes with fluctuating environmental risk factors. However, traditional Bayesian hierarchical models for large‐scale space–time data often face significant computational hurdles. Standard approaches [116], typically rely on Markov Chain Monte Carlo (MCMC) algorithms. While robust, MCMC scales poorly with the high‐dimensional datasets common in modern geostatistics, often leading to a “computational bottleneck” where the time required for model convergence makes real‐time analysis impractical. As highlighted in Banerjee et al. [117], the complexity of calculating spatial processes for massive numbers of locations—the ““Big Data problem””—requires specialized hierarchical structures and approximations. These models are frequently implemented in probabilistic programming languages such as Stan [118], which provides a flexible interface for Bayesian inference but remains subject to the sampling‐time constraints and convergence issues of MCMC in high‐dimensional settings.

Amortized Bayesian inference addresses these challenges by shifting the computational burden from the “online” inference phase to an “offline” training phase, a paradigm shift facilitated by the use of deep generative models to learn global summary statistics and posterior estimators [119]. In the context of spatial and environmental sciences, this approach has been advanced through neural Bayes estimators for high‐dimensional processes [120] and the development of neural proximity graphs for rapid spatial inference. By leveraging these neural architectures to learn the mapping from observed data directly to posterior distributions, amortized frameworks allow for near‐instantaneous statistical analysis [121]. This capability is essential for delivering the real‐time insights needed for immediate public health responses, such as issuing alerts for rapid shifts in air quality or forecasting acute disease outbreaks.

A significant advancement in this area is the development of dynamic Bayesian learning for spatial–temporal mechanistic systems [122]. This approach involves the “emulation” of computationally intensive physical models, such as chemistry transport models (CTMs), using Gaussian processes that can scale to massive data by operating sequentially over streaming subsets of data. By melding physical dispersion laws with observed sensor data, these statistical emulators allow for high‐resolution exposure estimates without the traditional computational costs of numerical differential equation solvers, facilitating the space–time analysis of dynamic physical processes as discussed by Cressie and Wikle [123].

Building upon these foundations, the convergence of GeoAI and Bayesian transfer learning offers a sophisticated solution for the pervasive problem of data scarcity in specific geographic regions. These models utilize knowledge from a data‐rich “source” domain to inform a data‐poor “target” domain, ensuring that marginalized communities or rural areas receive the same level of analytical precision as well‐instrumented urban centers. Furthermore, Bayesian predictive stacking [124] has emerged as a promising approach for addressing the change of support or spatial–temporal misalignment problems inherent in environmental epidemiology [125]. This involves reconciling point‐level air quality measurements with area‐level health outcomes, such as census‐tract‐level hospital admission rates.

Rather than relying on a single “best” model, stacking assigns weights based on predictive performance—a framework popularized by Yao et al. [126]—providing a more robust estimation of risk than traditional interpolation methods. Ultimately, these advanced Bayesian frameworks can facilitate a transition toward a more comprehensive understanding of the exposome. Techniques like Bayesian Kernel Machine Regression (BKMR) or profile regression [127], when enhanced by amortized inference and mechanistic emulation, allow AI to estimate the synergistic effects of chemical, physical, and biological stressors. By identifying critical windows of vulnerability through these automated yet physically grounded models, the field can design more effective, evidence‐based interventions for managing chronic conditions like asthma and atopic dermatitis tailored to the specific spatial–temporal context of an individual's exposures.

Finally, advanced AI methods such as causal AI are enhancing our understanding of causal relationships. Unlike conventional AI, which detects patterns and correlations in historical data to forecast outcomes, causal AI aims to elucidate cause‐and‐effect mechanisms. It prioritizes transparency and decision‐centric learning, often utilizing smaller, structured datasets to facilitate strategic planning and reasoning. In contrast, traditional AI typically depends on large datasets and functions primarily as a black box, providing predictions without explaining underlying causal mechanisms. For instance, a study conducted in the United Kingdom employed a Bayesian causal DL framework to assess the time‐varying causal impacts of fine particulate matter (PM2.5) and public health interventions on COVID‐19 infection rates. The findings suggested that short‐term exposure to PM2.5 significantly increased infection rates. Additionally, the study identified school closures as most effective during early waves, while closures of public transport became critical during later phases [128].

8. Conclusion

The adoption of AI in environmental health science offers transformative potential for data analysis, outcome prediction, and intervention implementation. These technologies have demonstrated effectiveness in estimating environmental exposures, predicting disease outcomes, and elucidating complex causal pathways. Their utility extends to advancing pollution research, improving chemical safety evaluations, and delivering real‐time disaster response solutions (Figure 5; Table 2). AI is integral to addressing pressing environmental issues, including optimizing carbon capture and waste management, as well as remediating contaminated sites. By harnessing AI's predictive capabilities, environmental health strategies can become more proactive and precise, potentially yielding significant public health benefits. However, while benefiting EHS, AI has a large environmental impact. Data centers produce electronic waste, consume large amounts of water, rely on critical minerals and rare elements, and use massive amounts of electricity, spurring the emission of planet‐warming greenhouse gases. These environmental issues need to be addressed [131, 132, 133, 134].

FIGURE 5.

Summary of concepts discussed in the manuscript regarding the applications of artificial intelligence, machine learning, and deep learning in exposomics, exposure assessment for environmental impacts, climate disaster prediction and response, environmental mitigation strategies, environmental justice, and health effects as well as personalized medicine.

TABLE 2.

Notable examples of AI applications in environmental health sciences.

Areas of AI applications	Notable examples	Citations
Exposure Assessment for Environmental Health Impacts	AI models predict PM2.5 and ozone levels using satellite data and validation with sensor data.	[50, 51, 52]
	ML models have been used to predict arsenic and lead levels in drinking water from limited exposure data.	[47, 48, 49]
	AI models can be used to improve prediction of toxicity for new chemicals using in vitro assays and omics data.	[9, 10]
Responding to Climate‐Related Disasters	ALERTCalifornia and other multi‐modal AI models are being used to detect wildfires in real‐time, improving firefighting response times.	[58] https://alertcalifornia.org/
	ML models environmental and meteorological effects on asthma‐prone populations.	[55, 56]
	EN‐ROADS simulator models climate‐related disaster scenarios for policy recommendations.	[60]
Investigating Environmental Mitigation Strategies	Deep neural networks and ML predict efficiency of carbon capture under varying conditions.	[66, 67, 68]
	ML integrates computer vision to improve identification and segregation of recyclable materials	[69, 70, 71]
	Predictive analytics help forecast waste generation patterns, optimize collection routes, and improve recycling processes.	[72, 73]
Exposome and Environmental Health	The envisioned Human Exposome Project called for the use of AI to discover unknown exposure‐health links for personalized prevention strategies.	[2]
Health Effects and Personalized Medicine	Deep learning algorithms predict asthma exacerbations based on air pollution, weather, and pollen levels.	[35]
	AI can enhance drug design and development by predicting drug responses and stratifying patients for therapeutic interventions using genomic and biomarker data.	[88, 89, 90, 91, 92]
	AI methods predict organ toxicity from chemical exposures.	[87]
Environmental Justice	ML has been used to predict water quality violations which can disproportionately affect marginalized communities.	[97, 98]
	AI Models reveal how air pollution disproportionately affects socio‐demographically disadvantaged groups.	[129]
	AI model evaluates racial covenants in California property deeds with high accuracy, saving significant human effort.	[130]

Nonetheless, challenges persist in integrating AI within this domain. Input data strongly influences machine learning outcomes across training, validation, and testing stages. During training, data quality, quantity, and representation determine how well the model learns patterns; poor or biased data leads to inaccurate learning. Validation data helps tune the model and detect overfitting, but must reflect real‐world distributions to provide reliable feedback. Test data evaluates final performance and generalization, revealing how the model handles unseen or diverse cases. Consistency in data distribution across all stages is crucial, as mismatches cause misleading results. Ultimately, clean, balanced, and representative input data ensures robust, fair, and accurate model [1].

Future healthcare AI laws should focus on shared responsibility across developers, hospitals, and clinicians rather than placing blame on a single party. Accountability should match who controls each stage, including design, deployment, and monitoring. Regulations should vary by risk, with stricter rules for high‐stakes or autonomous systems. Ongoing oversight, such as regular updates, performance checks, and audit trails, will be essential. Hospitals will also need clear governance systems to manage AI safely. Overall, effective legislation must remain flexible, transparent, and adaptable to ensure safe and fair use of AI as it becomes fully integrated into healthcare systems worldwide. Further, ensuring data quality, model interpretability, and adherence to ethical principles is essential to mitigate biases and promote equitable application. The accuracy, reliability, and transparency of AI models are crucial for building trust and enabling broader implementation in clinical and public health contexts. While AI holds considerable promise for advancing environmental health science, a balanced approach—encompassing vigilant oversight, ongoing technological development, and ethical safeguards—is imperative. Responsible utilization of AI capabilities can facilitate significant progress in environmental management and public health.

Author Contributions

All authors were involved with the design, drafting, and review of the manuscript.

Funding

This project is supported by the National Institutes of Health (grant no. T32 AI007512 to T.H.N., grant no. K24 AI106822 to W.P., grant nos. U01 AI147462, UM2AI130836, and P01AI153559 to K.C.N).

Conflicts of Interest

C.A. reports research grants on mechanisms of epithelial barrier integrity using AI from Scibase AG, Stockholm, grants from Seed Health, CA, USA; advisor at Seed Health, CA, USA Sweden; Director of SIAF, Editor‐in‐Chief Allergy. All other authors declare no conflicts of interest.

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.