Big data and deep learning in preventive and rehabilitation medicine

Abstract

The digitalization in medicine has led to almost universal availability of information to different healthcare professionals and accelerated clinical pathways. Fast-track concepts and short hospital stays require intelligent and practicable systems in preventive and rehabilitation medicine. This includes optimization of movement analysis by innovative tools such as detectors sensing skin movements, portable feedback systems for monitoring, robot-assisted devices, and prevention programs based on reliable data. Finally, clinical structures are needed to exploit the maximal potential of artificial intelligence (AI) and deep learning. One example is the establishment of inter- and transdisciplinary professional teams such as aRehaBoard. In contrast to other cost-intensive disciplines such as oncology, the introduction of AI into rehabilitation orthopedics and trauma surgery with the support of cross-sectoral cooperation has great potential for performing well in patient benefit-orientated competition (value-based competition).