Visualizing (Bio-)Medicine with Artificial Intelligence

We currently witness the impressive success of artificial intelligence (AI) in real-world applications, ranging from autonomous driving over speech recognition to the health care sector. At the same time, modern, typically data-driven AI methods have a similarly strong impact on science such as astronomy, physics, medicine – as well as humanities or social sciences, often replacing classical methods in the state of the art. In fact, at present, basically any research area is already impacted or starting to get involved in research questions in the realm of AI. However, despite this outstanding success, most of the research on AI is empirically driven and not only is a comprehensive theoretical foundation -- in particular, in the sense of explanations of decisions -- missing, but even the limitations of these methods are far from being well understood. It is also far from clear how AI-based methods can be optimally combined with classical methods based on physical models as domain knowledge.

At present, two general streams of research in artificial intelligence can be identified worldwide. On the one hand, existing methodologies are adapted and applied to diverse scientific areas, while on the other hand, researchers aim to tackle the aforementioned methodological/theoretical problems and initiate the next generation of artificial intelligence. At LMU Munich, those directions are also prominently represented and displayed at https://www.lmu.de/ai. It is important to also stress that in fact both directions require a highly interdisciplinary effort and have many interconnections.

The CAS Research Focus therefore aims to connect, in particular, more methodological/theoretical with more application-oriented researchers across all faculties of LMU Munich as well as existing research and teaching activities, focusing on the following key problem complexes at the verge of the next generation of AI:

    AI and Uncertainty
    AI and Domain Knowledge
    Limitations of AI
    Social Aspects of AI (Explainability, Fairness, etc.)

Science

We currently witness the impressive success of artificial intelligence (AI) in real-world applications, ranging from autonomous driving over speech recognition to the health care sector. At the same time, modern, typically data-driven AI methods have a similarly strong impact on science such as astronomy, physics, medicine – as well as humanities or social sciences, often replacing classical methods in the state of the art. In fact, at present, basically any research area is already impacted or starting to get involved in research questions in the realm of AI. However, despite this outstanding success, most of the research on AI is empirically driven and not only is a comprehensive theoretical foundation -- in particular, in the sense of explanations of decisions -- missing, but even the limitations of these methods are far from being well understood. It is also far from clear how AI-based methods can be optimally combined with classical methods based on physical models as domain knowledge. At present, two general streams of research in artificial intelligence can be identified worldwide. On the one hand, existing methodologies are adapted and applied to diverse scientific areas, while on the other hand, researchers aim to tackle the aforementioned methodological/theoretical problems and initiate the next generation of artificial intelligence. At LMU Munich, those directions are also prominently represented and displayed at https://www.lmu.de/ai. It is important to also stress that in fact both directions require a highly interdisciplinary effort and have many interconnections. The CAS Research Focus therefore aims to connect, in particular, more methodological/theoretical with more application-oriented researchers across all faculties of LMU Munich as well as existing research and teaching activities, focusing on the following key problem complexes at the verge of the next generation of AI: AI and Uncertainty AI and Domain Knowledge Limitations of AI Social Aspects of AI (Explainability, Fairness, etc.)

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Visualizing (Bio-)Medicine with Artificial Intelligence

Visualizing (Bio-)Medicine with Artificial Intelligence