Auf künstlicher Intelligenz basierende Ansätze zur Diagnostik von Nahrungsmittelunverträglichkeiten

Translated title of the contribution: Artificial intelligence-based approaches for diagnosis of adverse reactions to food

Julia Dreekmann, Anna Kordowski, Franziska Schmelter, Christian Sina*

*Corresponding author for this work


An increasing number of people worldwide suffer from adverse reactions to food (ARF). ARF can have both an immunological and a non-immunological background, which is relevant for both diagnosis and treatment. In everyday clinical practice, exact classification of ARF is sometimes challenging, as the symptoms can be relatively unspecific and overlap between ARF subgroups. In addition, some test systems frequently used in clinical routine have significant limitations. This concerns both their sensitivity and specificity as well as the relatively high resource demands. Use of artificial intelligence (AI) could represent a method to improve diagnosis of ARF in the future. Initial studies suggest that the use of AI can predict the individual risk of developing a food allergy as well as the allergic potential of new food proteins with a high degree of certainty. These and other examples of the successful use of AI applications in the diagnosis of ARF are encouraging and should provide an incentive for further studies.

Translated title of the contributionArtificial intelligence-based approaches for diagnosis of adverse reactions to food
Original languageGerman
Issue number1
Pages (from-to)35-41
Number of pages7
Publication statusPublished - 01.2024

Research Areas and Centers

  • Centers: Center for Artificial Intelligence Luebeck (ZKIL)
  • Research Area: Intelligent Systems

DFG Research Classification Scheme

  • 205-05 Nutritional Science, Nutritional Medicine

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