Leveraging Artificial Intelligence for Targeted Response in Allergic Rhinitis


  • Kaiser Jamil Genetics Department, Bhagwan Mahavir Medical Research Centre
  • Asim Uddin, M. Genetics Department, Bhagwan Mahavir Medical Research Centre
  • Shyam Gade ENT & Allergy Department Consultant, Mahavir Hospital and Research Centre, 10-1-1, Mahavir Marg, MasabTank, Hyderabad-500004, Telangana, India




One step further to Bioinformatics is Artificial intelligence (AI) and data analysis, one would wonder how we can apply AI in the area of aeroallergens’ interactions in Allergic rhinitis (AR). Allergic rhinitis, also known as hay fever, is a common condition that affects millions of people worldwide. It is characterized by the inflammation of the nasal passages due to an immune response triggered by allergens such as pollen, dust mites, and animal dander and many more aeroallergens. Allergic rhinitis is a growing public health, medical and economic problem worldwide. The treatment of allergic rhinitis involves the use of drugs, such as antihistamines, decongestants, and corticosteroids, which can interact with genes and affect the way they work. For example, drug metabolism can be influenced by polymorphic genes which are the first line of action to an external agent like drugs. Some people may metabolize drugs faster or slower than others due to their genetic make-up. While some people may have a genetic variant that makes them less able to metabolize antihistamines, which can lead to side effects such as drowsiness and dry mouth as it happens in AR. While traditional treatments exist, the emergence of artificial intelligence (AI) offers new opportunities for targeted and personalized interventions. This article explores the potential applications of AI in the management of allergic rhinitis and how it can improve patient outcomes. The symptoms of allergic rhinitis can significantly impact a person's quality of life, leading to sneezing, congestion, itching, and nasal discharge and affecting their daily routine.




How to Cite

Jamil, K., Asim Uddin, M., & Gade, S. (2023). Leveraging Artificial Intelligence for Targeted Response in Allergic Rhinitis. British Journal of Healthcare and Medical Research, 10(3), 281–283. https://doi.org/10.14738/bjhmr.103.14757