The presentation of the Interdisciplinary Colloquium on Digitalisation of Research "Transformation of Medical Care and Research Based on Digitalisation - Health 4.0" with Till Keller. The amount of information available to treating physicians as well as to life science researchers has grown massively within the last years. The increased level of detail and precision as well the multi-modal nature of data poses a challenge to the e.g. medical doctors focusing on important findings while integrating as much relevant information as possible. Approaches including e.g. artificial intelligence agents might help to improve informed decision making in daily clinical routine even in the light of workload consolidation. Besides medical care, machine learning offers great chances in medical research, especially in epidemiological research founded on large datasets. Real world medical data provides insights into authentic medical challenges, nevertheless this comes with specific issues like dealing with missing values or imbalance of cases. Further, ensuring bias-free model development can be challenging. Machine learning algorithms allow efficient evaluation of large (real-world) datasets in the context of existing knowledge and expectations but also might help to identify new aspects leading to ideas outside of the box of established medical research.