Main figure captions

Figure 1. Hierarchy of disciplines of artificial intelligence.The discipline of AI is often categorized as part of computer science, although it also builds upon other fields, such as mathematics and cognitive sciences. ML is a subdiscipline of AI, whereas deep learning is a further specialization within ML, generally characterized by large-scale artificial neural networks consisting of many layers, hence the term deep. The right part of the figure displays typical terms that one encounters within the (sub)discipline.
Figure 2. A conceptual framework for AI applications in the biomedical domain. The framework is structured by learning strategy, learning goal and data modality. The included studies are selected as illustrations of how AI is used within the medical field and how its applications can be conceptually categorized. They are not necessarily selected based on the inclusion criteria stated in the introduction. References for the shown studies are included in the Supplementary References.
Figure 3. Difference between ordinary least squares (OLS), machine learning (ML), and deep learning (DL) methodology. (a) In OLS, features (or predictors) are modeled manually, and their relationship is assumed linear to the output variable unless specified differently. Interpretation of the model and learned patterns (inference) is straightforward. (b) A similar procedure is followed with ML, but the algorithm can learn more complex patterns from the provided features. Nevertheless, thorough feature engineering by the practitioner is a critical step for delivering a performant model. (c) With DL, especially when applied to unstructured data, feature engineering is an inherent behavior of the interconnected neural network layers. The relationship between input features (tabular data fields, image pixels, text snippets, etc.) to the predicted output is more opaque and harder to interpret.
Figure 4. Workflow of developing a machine learning model to predict disease risk. The best practice in machine learning modeling is using distinct training, validation (or tuning), and test datasets. The modeling steps till testing are generally executed in sequential order, where it is common to iterate multiple times based on validation results that inform model improvements. It is discouraged to assess and improve test performance iteratively, as this can lead to overfitting. The steps preceding model development are excluded, primarily consisting of problem definition and study design, data collection, and preprocessing.
Figure 5. Application domains of artificial intelligence within the allergy field. Domains identified as currently most active and discussed in the main text. Other areas, such as clinical trial optimization, have been excluded due to the limited number of impactful applications.