Abstract
The field of medicine is witnessing an exponential growth of interest in
Artificial Intelligence (AI), which enables new research questions and
the analysis of larger and new types of data. Nevertheless, applications
that go beyond proof of concepts and deliver clinical value remain rare,
especially in the field of allergy and immunology. This narrative review
provides a fundamental understanding of the core concepts of AI and
critically discusses its limitations and open challenges, such as data
availability and bias, along with potential directions to surmount them.
We provide a conceptual framework to structure AI applications within
this field and discuss forefront case examples. Most of these
applications of AI and machine learning in allergy concern supervised
learning and unsupervised clustering, with a strong emphasis on
diagnosis and subtyping. A perspective is shared on guidelines for good
AI practice to guide readers in applying it effectively and safely,
along with prospects of field advancement and initiatives to increase
clinical impact. We anticipate that AI can further deepen our knowledge
of disease mechanisms and contribute to precision medicine in allergy.