A systematic review of FDA-cleared surgical robots from 2015 to 2023 reveals that most are at Level 1 (Robot Assistance), with some reaching Level 3 (Conditional Autonomy). The study introduces the Levels of Autonomy in Surgical Robotics (LASR) classification to categorize robotic systems based on their autonomy. The review highlights trends toward greater autonomy in surgical robotics, emphasizing the need for updated regulatory frameworks to ensure safe and effective integration. Surgical robots range from systems requiring continuous surgeon control to those that can generate patient-specific plans and perform tasks autonomously. The FDA primarily uses the 510(k) pathway for regulatory approval, but a growing number of systems are cleared via the De Novo pathway. The review also notes the increasing integration of machine learning in surgical robotics, which challenges the traditional view that surgical robots lack autonomy. The study underscores the need for a unified framework to address the evolving roles of surgeons, manufacturers, and regulatory agencies in ensuring the safety and effectiveness of autonomous surgical systems. The findings suggest that current regulatory frameworks may not adequately address the increasing autonomy of surgical robots, necessitating the development of more specific standards and guidelines. The study concludes that a clear framework for surgical robotics is essential to promote procedural safety and liability management as the field continues to evolve.A systematic review of FDA-cleared surgical robots from 2015 to 2023 reveals that most are at Level 1 (Robot Assistance), with some reaching Level 3 (Conditional Autonomy). The study introduces the Levels of Autonomy in Surgical Robotics (LASR) classification to categorize robotic systems based on their autonomy. The review highlights trends toward greater autonomy in surgical robotics, emphasizing the need for updated regulatory frameworks to ensure safe and effective integration. Surgical robots range from systems requiring continuous surgeon control to those that can generate patient-specific plans and perform tasks autonomously. The FDA primarily uses the 510(k) pathway for regulatory approval, but a growing number of systems are cleared via the De Novo pathway. The review also notes the increasing integration of machine learning in surgical robotics, which challenges the traditional view that surgical robots lack autonomy. The study underscores the need for a unified framework to address the evolving roles of surgeons, manufacturers, and regulatory agencies in ensuring the safety and effectiveness of autonomous surgical systems. The findings suggest that current regulatory frameworks may not adequately address the increasing autonomy of surgical robots, necessitating the development of more specific standards and guidelines. The study concludes that a clear framework for surgical robotics is essential to promote procedural safety and liability management as the field continues to evolve.