The Potential Role of Artificial Intelligence in Allergy and Rhinology: A Paradigm Shift

Dear Editor,

The prevalence of allergic and rhinologic diseases is increasing worldwide in people of all age groups and demographics. Traditional methods of diagnosis are based on complete patient history and physical examination supplemented with imaging methods like nasal endoscopy when needed. However, there is a disparity in the diagnosis and treatment of these conditions. Artificial intelligence (AI) has been emerging as a valuable resource in the field of allergy and immunology over the course of the past decade. While conventional methods of diagnosing allergies and rhinological conditions require a specialist and are subjective and difficult to achieve due to the problems in classifying the responsible allergen, their heterogeneity in presentation, and their varying severity, complex AI algorithms, on the contrary, take into consideration the environmental, clinical, and demographic data to diagnose and predict possible exacerbations, thus helping in minimizing the healthcare resource allocation due to poor control of these conditions affecting a large population. ¹ For instance, AI has been able to differentiate allergic atopic dermatitis from other skin diseases by analyzing the images of the skin lesions and predicting the severity of the allergy by taking into account the data from the patient’s serum biomarker. ¹ Another example is the AI-operated model for pollen detection, which help classify allergies caused by different species of pollen. ² AI is also implemented in imaging data to diagnose sinusitis from anatomical variations. ¹ While clinicians often find it difficult to diagnose asthma in pediatric patients below 5 years of age based on presenting symptoms as they are mostly self-resolving, an AI model fed with the clinical and demographic data of patients diagnosed with asthma below 5 was able to predict the continuance of asthma with an accuracy of 81%. ¹ A continent-wide project has been implemented in Europe to collect, analyze, and interpret data at the molecular level to understand the pathogenesis of various allergic diseases and biomarkers which will be useful in determining the course and responsiveness to treatment. ²

The treatment of allergic and rhinological diseases is to avoid the allergen itself, which when not possible focuses on symptom-based, personalized therapy along with general anti-allergy medicaments. AI has been able to predict patients who are at risk of repudiating a certain line of treatment and provide alternate care options. ³ In the case of allergic rhinitis, InHandPlus has designed a smartwatch, a wearable AI device, which records a mini video and analyses the pattern of medication intake behavior and keeps check on the continuity of medicine intake, the lack of which is the most common cause and symptoms in patients with seasonal allergies. ⁴ This device can also differentiate between the form of medicine, from oral antihistamines to nasal corticosteroids or eye drops, and the intake pattern and report it to the physician if the patient does not follow the course for more than 2 days. ⁴ The results of a parallelly controlled, randomized study to check the constancy of medicine-taking behavior using AI showed that people using the wearable AI device had high adherence to prescribed oral antihistamines and minimal symptoms of allergy during the pollen season. ⁴ The major limitation of using AI analysis in the field of allergy and rhinology is the heterogeneity in presentation and response to therapy which varies in a diverse population. More complex algorithms that can self-learn and adapt are required to analyze the variabilities and provide a more individualized idea for care. However, there are huge developments regarding utilizing AI in rhinology to differentiate allergic conditions from non-allergic diseases or malignancies. ⁵ Lastly, healthcare professionals must be trained to operate and implement the interpretations made by the AI analysis.

AI could also play a significant role in estimating the impact of air quality, especially on particulate matter and the development of respiratory diseases. Real-time monitoring: IoT-based indoor air quality and health monitoring systems can collect data on air quality and respiratory health in real-time. ⁶ AI algorithms can then analyze this data to identify patterns and predict future air quality and health outcomes. Air quality prediction: AI models can be trained on historical air quality data to predict future air quality levels. ⁷ This can help individuals and organizations take preventive measures to reduce exposure to harmful pollutants. Fine particulate matter estimation: AI can improve the accuracy of models that estimate the concentration of fine particulate matter in urban air. ⁶ Few AI models were developed that could estimate fine particulate matter levels with greater detail and accuracy than traditional models.^6,7 Overall, AI can help us better understand the impact of air quality on respiratory health and take preventive measures to reduce exposure to harmful pollutants.

In conclusion, AI has the potential to achieve more precision in the diagnosis of allergies and rhinological diseases by cumulating personal history, environmental factors, and genetic information to predict the possible occurrence or continuity of periodic or consistent allergic crises. It can also aid in keeping track of the attack pattern and control of symptoms. However, it is difficult to provide individualized solutions to all forms of allergic or rhinologic conditions due to the disparity in its presentation in a diverse population. AI should be considered a helping tool by healthcare professionals to maximize the quality of patient care.