# Review of Advanced Medical Telerobots
## 1 Introduction
### The Motivations for Telebotics in Medicine
This paradigm for delivering intensive active-assist therapy attracts more interest recently due to the increased awareness caused by the COVID-19 pandemic. Without such technology, patients who may be considered at high-risk would potentially receive a degraded quality of therapy during a global health crisis (such as a pandemic), due to the limitations around accessibility to public places and requirements regarding reducing the frequency of interpersonal interactions in the healthcare systems. This topic is more discussed in Section 2. It should also be mentioned that the conventional use of a teleroboticsystem enables direct supervision of the operator over the conduction of the task. Indeed, more advanced telerobotic systems have embedded local intelligence allowing for the realization of shared-controlled tasks (see Section 3.2), during which the operator and the machine share the autonomy for task conduction. Several examples include telerobotic systems which compensate for the surgeon's hand tremor during operation, scale down the surgeons motor control to enhance the precision, provide forbidden and guiding virtual fixtures, or compensate for the movement of the organs during operation. The shared autonomy frameworks are designed to reduce the mental and physical burden on the surgeon and maximize the quality of surgery up to the level which is not possible to achieve without the use of a telerobotic system. Some examples can be found in [65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76]. Another interest of surgical telerobots is their ability to help train surgeons. For instance, in [77], the daVinci robot is able to record and simultaneously playback laparoscopic videos, robot arm motions, and surgeon-console interactions. A user can then replay on-demand recorded procedure sections with the opportunity to watch stereo videos and simultaneously feel the recorded movements on the hand controllers, at adjustable speeds. This approach has been recently used in [78] to determine experimentally how a surgical robotic system can be used for training novice surgeons in conventional laparoscopic. These authors show that combining the playback function and discovery training (trainees learned a surgical task by themselves through trial and error) leads to the best accuracy compared with trainees who used playback or discovery alone. It is valuable to record surgical robot usage by surgeons to collect data for several purposes: (1) training purposes as exposed earlier, (2) the development of advanced control algorithms enhancing the autonomy levels of surgical robots, (3) retrospective error analysis [77]. Yet in 2017, [PERSON] et al., envisioned that all the objective data available (with patient-privacy constraints) provided by these recordings and surrounding sources (such as medical imaging systems) could be used through big-data analysis for the three aforementioned purposes. For example, surgical data science should therefore _\"provide better patient outcomes and a reduction in healthcare costs\"_. An example of such approach is provided by [PERSON] et al., in [79]. They experimentally showed the efficiency of machine learning to provide automated skill assessment in surgical training using public MIS robotic datasets.