AI & Semantics

Analyzing large volumes of medical data for clinical decision-making requires not only the identification of statistical relationships but also an understanding of causal relationships in biological processes. The use of causal and data-driven models makes these analyses more robust and easier to understand. Machine learning methods bridge the gap between clinical information, imaging, laboratory test results, and electronic health records to provide a holistic view of patients. The development of evidence synthesis methods that integrate heterogeneous data sources and are based on counterfactual methods, meta-analysis, and artificial neural networks shows great promise. The formation of early-career research groups on the topics of AI and Causality, AI and Bioinformatics, AI Systems in Multi-Omics Data, and Statistical Evidence in AI Systems is intended to tap into the potential in the field of “AI and Semantics.”