The Surgical Data Science applies data management and processing approaches including machine learning on medical data to provide optimal, individualized and contextual support to the surgeon. To provide the surgeon with all necessary information in an adequate manner, novel imaging sensors as well as techniques for multimodal sensory feedback and augmented reality are investigated. To minimize the patient trauma while optimizing the dexterity of the surgeon, miniaturized instruments and semi-autonomous assistance functions are developed. The current research in Robotically Assisted Surgical Systems (RASS) increasingly uses established robotic platforms. Therefore, the work at hand is considered as an introductory paper into the topic and especially addresses investigators, researchers, medical device manufacturers, and clinicians, who are new to this field. Comprehensiveness cannot be achieved in this format, but besides the general overview, references to further readings and more comprehensive reviews with regard to particular aspects are given. This paper provides an overview of the general concepts of robotically assisted surgical systems, briefly revisiting historical and current developments in the surgical robotics market and discussing current focus areas of research. Thus, they play an increasingly important role in patient care. Robotic assistance systems for diagnosis and therapy have become technically mature and widely available.
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