Thomas Krause M.Sc., Dr. Elenar Jolkver, Prof. Dr. Michael Kramer, Prof. Dr. Matthias L. Hemmje, Prof. Dr. Paul McKevitt
Genomic data is an important building block for the era of personalized medicine. However, processing this data efficiently in diagnostic laboratories faces several challengesin distinct areas such as big data, artificial intelligence, regulatory environment, medical/diagnostic standards (evolving guidelines), and software requirements engineering. Analysis of the state of the art in these areas shows promising approaches and suitable reference models but no direct solutions. Existing technical solutions for genomic data analysis tend to be specialized for research projects and do not take into account the requirements for routine medical diagnostics including the regulatory constraints in this area. This chapter introduces a technical architecture for the GenDAI (Genomic applications for laboratory Diagnostics supported by Artificial Intelligence) project that aims to create a platform for genomic data analysis that is specifically tailored to the needs and requirements of laboratory diagnostics. This includes the automation of processes using data analysis pipelines and artificial intelligence.