Artificial Intelligence: Exploring the Future of Innovation in Allergy Immunology

Abstract

Purpose of Review Artificial intelligence (AI) has increasingly been used in healthcare. Given the capacity of AI to handle large data and complex relationships between variables, AI is well suited for applications in healthcare. Recently, AI has been applied to allergy research. Recent Findings In this article, we review how AI technologies have been utilized in basic science and clinical allergy research for asthma, atopic dermatitis, rhinology, adverse reactions to drugs and vaccines, food allergy, anaphylaxis, urticaria, and eosinophilic gastrointestinal disorders. We discuss barriers for AI adoption to improve the care of patients with atopic diseases. Summary These studies demonstrate the utility of applying AI to the field of allergy to help investigators expand their understanding of disease pathogenesis, improve diagnostic accuracy, enable prediction for treatments and outcomes, and for drug discovery.

Grand challenges in genetics and epidemiology of allergic diseases: from genome to exposome and back

“…Avoiding confusion and overcoming the limitations of artificial intelligence (bias, harm, ethical considerations, regulations, etc.) is a major challenge that we are beginning to face ( 34 ).…”

Prospects for developing allergen‐depleted food crops

“…By analyzing the structure and function of allergenic proteins, AI algorithms can identify potential targets for genetic engineering or protein modification that can reduce allergenicity while maintaining the nutritional value of the food. The scope of AI and bioinformatics for reducing plant food allergens is vast and includes applications in genetic analysis, allergen identification, and production optimization (MacMath et al, 2023;Solanki et al, 2020). These technologies have the potential to revolutionize the field of plant food allergen reduction, leading to safer and more accessible food for individuals with allergies for their health and well-being.…”

APAAACI 2023 International Conference: The innovation revolution in allergy, asthma, and immunology

“…DL is a subset of ML that uses layers of neural networks to process complex data. NLP is a method of computer-based analysis of unstructured text, for example, data mining from clinicians’ notes [ 8 ]. Generative AI describes algorithms that can be used to create new content, including audio, code, images, text, simulations, and videos, for example, ChatGPT (GPT stands for generative pretrained transformer).…”

“…Similar trends are observed in Conformité Européene-marked medical devices incorporating AI within the European Union [ 10 ]. More recently, AI has also been applied to allergy research in asthma, atopic dermatitis, rhinology, adverse reactions to drugs and vaccines, food allergy, anaphylaxis, urticaria, and eosinophilic gastrointestinal disorders [ 8 , 10 ]. In future, AI will improve our understanding of patient profiles, disease mechanisms, risk stratification, and precision diagnosis and aid in the management of allergic and immunologic disorders.…”

“…Tutorials to harness electronic medical records (EMR) and AI will help clinicians improve their diagnostic and therapeutic capabilities, improve efficiency within the healthcare system, support physician work and lifestyle preferences, and reduce time spent on EMR [ 11 ]. However, given that elements of bias, ethics, and harm may be inappropriately incorporated at many levels of AI and ML modeling, there are many regulatory, governance, privacy, data, and legal considerations that need to be considered for patient safety before AI can be implemented as standard of clinical care within a specialty [ 8 ].…”