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Comprehensive Lifesciences Neural Information Computing 5.1 - outcome-based patient care through AI-defined interventions with the virtual patient in 4D



In everyday clinical practice, physicians are faced with numerous decisions that they must make for the well-being and best possible therapeutic outcome of the respective patient. Unfortunately, it is an unchanged core problem of medicine that for many disease entities the individual therapeutic success can only be predicted with a general statistical probability. A prognosis for the individual patient, on the other hand, remains completely unclear in that sense. Thus, it cannot be predicted exactly how the individual patient will react to a specific therapy. The heuristic method of consecutive "trial-and-error" treatment very often results in a valuable loss of time and resources for patients, which can also have a negative impact on the therapy outcome.



In the CLINIC 5.1 project, industry and research institutes are developing new and marketoriented forms of AI-based decision support in medicine based on the use cases of urological tumour diseases. In this way, innovative tools for decision support based on previous studies, diagnoses and therapies can be established in all phases of diagnostics, therapy recommendation and therapy implementation - a forward-looking step towards personalised medicine including new patient-oriented and ethical treatment strategies with the possibility of individually tailored therapy recommendations. Within the CLINIC 5.1 project, the available diagnosis- and therapy-relevant data and information will be expanded and multilaterally integrated in order to be able to map the respective patient four-dimensionally, i.e. also longitudinally, and virtually as accurately as possible. The high-quality and medically and economically valuable treatment data of prostate tumour patients collected and curated at Heidelberg University Hospital, some of which has been available for almost thirty years, is an example of "data as an economic asset" and will enable AI algorithms to be trained efficiently and effectively.