Introduction

As of February 2023, the US Food & Drug Administration (FDA) has approved 521 medical applications that utilize Artificial Intelligence (AI) and Machine Learning (ML). Most of these (75%) are in radiology, followed by cardiology, hematology and neurology. Similar trends are observed in Conformité Européene (CE)-marked medical devices incorporating AI within the European Union. Currently, no registered AI and ML-based applications are being utilized in the field of allergy and immunology. One can therefore question if this field is missing out on new research opportunities and clinical applications either because of insufficient access to AI applications or a lack of awareness of potential applications. However, given the rapid pace of technological advances, it can be anticipated that AI and ML algorithms will be increasingly applied in allergy and immunology soon.
Over the past decade, medicine has witnessed an exponential growth of interest in AI and the yearly number of scientific articles on AI has increased tenfold since 2012. This trend is fueled by the explosion of (bio)medical data, including multi-omics, image data, and digital electronic health records (EHRs), along with advancements in computing power. These developments have paved the way for advanced analytical approaches to address new research questions on large-scale datasets. Traditional analytical techniques are no longer adequate to handle such data complexity, volume, and structure. The introduction of accessible software and methodological advancements in AI have further promoted the use of AI in the (bio-)medical field. Most importantly, ML and AI can identify complex patterns in vast amounts of data, such as images, text, or audio and deliver superior predictive power, often surpassing traditional statistical methods.
This review provides a fundamental understanding of ML and AI’s core concepts. A framework is presented to structure the broad umbrella term AI, and an overview of several state-of-the-art applications of AI in medicine and allergy and immunology, specifically, is provided. The focus is on applications that preferably adhere to any, and ideally multiple, of the following conditions: (1) are externally or prospectively validated, (2) demonstrate a positive effect on clinically relevant patient outcomes, (3) FDA and/or CE approval, (4) outperform traditional methods, and (5) answer research questions where traditional analytical techniques fail. Additionally, we critically discuss the limitations and open challenges of AI applications and share an outlook on good practices of AI and ML in allergy and immunology.