Medicine and biomedical research are in a deep crisis. We hardly understand the causes of any disease. Not knowing the cause makes disease not curable; thus, they become chronic with increasing costs for society. Not knowing the causes of disease leaves the pharmaceutical industry with the only option to treat or modulate the symptoms of disease, an approach which since the 1950s is getting constantly more and more inefficient, risky and costly. Even when a drug is registered, most patients have no benefit from it and two thirds of all new drugs have no benefit at all. Our knowledge gaps in disease also affect biomedical research. The dogma “from cells to animals to human to patient benefit” is broken at several points. How can we know whether an animal model of disease is relevant to study a human disease when it maximally mimics the symptom of that disease, but we do not know when the causes for these similar symptoms are the same? On top of that there are quality issues. More than 50% of published peer-reviewed biomedical research results are not reproducible. Almost no research is ever attempted to be applied for patient benefit and the success rate is 1 in 23,000 publications or less. The key cause for this dead-end road we are in is our definition of ‘disease,’ namely by organ. This is how Medicine and biomedical research has been structured for more than a hundred years. For every organ we have a clinic, a specialist and a research discipline. This assumes that a symptom in one organ of a patient can have nothing to do with symptoms in another organ of the same patient. So, we make two diseases out of them, which we both do not understand and cannot cure. There is, however, a group of diseases where we know the cause, i.e. rare diseases. They are typically not named after an organ or symptom but a specific gene or protein (the product of a gene), which is a very precise disease definition allowing for a precise and even potentially curative intervention, e.g. gene therapy. Also, these often single-gene diseases cause symptoms in more than one organ; our current approach to chronic diseases would easily make several “independent” diseases out of each symptom. All of this we want to change, exemplified in a focus area of a set of diseases causing symptoms in the heart, blood vessels, brain and lung. We want to reduce the use of animals for this as much as possible and thus begin with existing human data. In the end we want to cure humans and not mice or rats. We do this by advanced bioinformatic “big data” methods revealing the underlying common causal mechanism of these co-occurring symptoms. Next, we develop diagnostics to pick out those patients that share both one or more symptoms and we can also detect in a simple blood sample that this underlying causal disease mechanism is affected. In parallel we look at whether drugs are available to treat this mechanism. Here we prioritise the already registered drugs because this provides the safest and fastest way to clinical application as lengthy and risky drug discovery is not necessary. We then validate these new precision diagnoses and precision therapeutic intervention first in healthy volunteers, then in patients. Thus, we reduce the uncertainty and vagueness of many current disease definitions to change medicine from imprecision to precision, from chronic to cure - and ideally to prevention.