Each SDL is supported by a competence team of computer scientists in the fields of artificial intelligence (AI) and machine learning tools with expertise in the development of foundation models, machine learning algorithms and customized automation. The aim of the AI Lab is cross-scale communication and information processing in the SDL network for the establishment of a data-driven design-build-test-learn workflow. The focus here is on data processing and model generation for the operation of the SDL network, which control both the interaction between machines and between machines and humans. This includes communication across distances and scales. Research will also be conducted into the efficiency of (collective) information processing in the form of foundation models and communication between machines.

The development of generative AI and machine learning models today is based on large amounts of already published data in biomedical research, which are often poorly annotated, heterogeneous or incomplete. However, in order to scale not only digital twins from the cell to the "virtual human twin" or the "digital patient twin" in the future, which characterize every microphysiological process down to the individual in high quality and which drive not only personalized medicine but also the development of innovations in medical technology, we must generate high-quality data in the future with which we are able to train the data collected so far in order to make high-quality statements. However, obtaining high-quality data also requires us to rethink how we design experiments in biomedical research.

More is coming soon!