The integration of robotics in surgery has increased over the past decade, and advances in the autonomous capabilities of surgical robots have paralleled that of assistive and industrial robots. However, classification and regulatory frameworks have not kept pace with the increasing autonomy of surgical robots. There is a need to modernize our classification to understand technological trends and prepare to regulate and streamline surgical practice around these robotic systems. We present a systematic review of all surgical robots cleared by the United States Food and Drug Administration (FDA) from 2015 to 2023, utilizing a classification system that we call Levels of Autonomy in Surgical Robotics (LASR) to categorize each robot’s decision-making and action-taking abilities from Level 1 (Robot Assistance) to Level 5 (Full Autonomy). We searched the 510(k), De Novo, and AccessGUDID databases in December 2023 and included all medical devices fitting our definition of a surgical robot. 37,981 records were screened to identify 49 surgical robots. Most surgical robots were at Level 1 (86%) and some reached Level 3 (Conditional Autonomy) (6%). 2 surgical robots were recognized by the FDA to have machine learning-enabled capabilities, while more were reported to have these capabilities in their marketing materials. Most surgical robots were introduced via the 510(k) pathway, but a growing number via the De Novo pathway. This review highlights trends toward greater autonomy in surgical robotics. Implementing regulatory frameworks that acknowledge varying levels of autonomy in surgical robots may help ensure their safe and effective integration into surgical practice.

Since opportunities for spaceflight experiments are scarce, ground-based microgravity simulation devices (MSDs) offer accessible and economical alternatives for gravitational biology studies. Among the MSDs, the random positioning machine (RPM) provides simulated microgravity conditions on the ground by randomizing rotating biological samples in two axes to distribute the Earth’s gravity vector in all directions over time. Real-time microscopy and image acquisition during microgravity simulation are of particular interest to enable the study of how basic cell functions, such as division, migration, and proliferation, progress under altered gravity conditions. However, these capabilities have been difficult to implement due to the constantly moving frames of the RPM as well as mechanical noise. Therefore, we developed an image acquisition module that can be mounted on an RPM to capture live images over time while the specimen is in the simulated microgravity (SMG) environment. This module integrates a digital microscope with a magnification range of 20× to 700×, a high-speed data transmission adaptor for the wireless streaming of time-lapse images, and a backlight illuminator to view the sample under brightfield and darkfield modes. With this module, we successfully demonstrated the real-time imaging of human cells cultured on an RPM in brightfield, lasting up to 80 h, and also visualized them in green fluorescent channel. This module was successful in monitoring cell morphology and in quantifying the rate of cell division, cell migration, and wound healing in SMG. It can be easily modified to study the response of other biological specimens to SMG.

We have developed a wireless system suitable for acquiring gastric slow wave activities and delivering electrical pulses to the stomach. The system is composed of a physically miniaturized front-end that can record slow waves from 3 channels and transmit the data to a back-end connected to a computer. A custom-made graphical user interface can display the slow waves in real-time and store them for off-line analysis. The user can turn on a switch on the back-end to activate electrical stimulation capability on the front-end. The electrical stimulation on the front-end is fixed at 4 mA with a pulse width of 300 ms. The front-end measures 13×44×4 mm3, allowing future implantation. The system performance was successful in bench-top testing and will be validated in animal models.

Neuroendoscopy is a minimally invasive surgical approach with numerous benefits, but it presents technical challenges such as spatial constraints and the need for precise, repetitive movements. While surgical robots have the potential to address these challenges, their adoption in neuroendoscopy remains limited due to inadequate user interfaces and reliance on overly specific preprogrammed commands. This work introduces a robot learning algorithm that enables surgeons to kinesthetically train a robot arm to navigate and operate a neuroendoscope. Surgeons can manually guide the robot arm through task demonstrations, make real-time corrections, and teach the robot preferred surgical approaches, all within the surgical workspace constraints to ensure patient and surgeon safety. We aim to develop this machine learning-driven approach to create an intuitive shared control system between the surgeon and the robot, potentially reducing task completion times and enabling adaptation to anatomical variations.