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As artificial intelligence (AI) plays an ever-increasing role in medicine, various designs that implement machine learning (ML) are being introduced in an effort to develop surgical robots that perform a variety of surgical techniques without human interference. However, current attempts in creating autonomous surgical robots (ASRs) are hindered by the amount of time needed to train a robot on a physical sett, the incredible amount of physical and/or synthetic (artificial) data needed to be collected and labeled, as well as the unaccountable and unpredictable characteristics of reality. Progress outside of the medical field is being made to address the general limitations in autonomous robotics. Herein, we present a review of the basics of machine learning before going through the current attempts in creating ASRs and the limitations of current technologies. Finally, we present suggested solutions for these limitations, mainly data driven physics simulations and domain randomization, in an attempt to create a virtual training environment as faithful to and as random as the real world that could be transferred to a physical setting. The solutions suggested here are based on techniques incorporated and strides being made outside of the medical field that could usher in the next generation of autonomous surgical robotics designs. Keywords:  Autonomous surgical robots; robotic surgery; artificial intelligence; machine learning; physics simulation; domain randomization

clinical surgery research communications

Ushering in the next generation of autonomous surgical robots? current trends and future possibilities with data driven physics simulation and domain randomization