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Sadagopan et al. Art Int Surg 2024;4:387-400 https://dx.doi.org/10.20517/ais.2024.34 Page 389

Surgical Inc. created the da Vinci Surgical System, which received Food and Drug Administration (FDA)
[13]
approval in 2000, revolutionizing minimally invasive surgery with unparalleled precision and control .
Robotic-assisted surgery continues to evolve with innovations such as improved haptic feedback, real-time
imaging, and augmented reality, significantly enhancing surgical outcomes and patient safety.

The integration of DL into robotic systems has led to automated surgical systems capable of enhancing
surgical precision and efficiency and making decisions intraoperatively. These systems utilize sensor fusion,
combining data from multiple sources such as cameras, force sensors, and navigation systems to provide
comprehensive situational awareness, while advanced control algorithms enable real-time adjustments to
the robotic instruments . The advancements in machine learning (ML) and DL have shifted the paradigm
[14]
from developing robots governed by a master-slave framework, to autonomous surgical systems capable of
aiding and making intraoperative decisions. The first autonomous surgery was conducted with the smart
tissue autonomous robot (STAR) in 2016 for bowel anastomosis . This system conducts the surgery
[15]
autonomously, only needing a human surgeon to approve its plan at the start of the procedure, and
throughout its duration if correction was needed. TSolution one is another autonomous surgical system that
can drill and carve bone for knee replacement surgery according to a predetermined plan but cannot
distinguish between types of tissue . Thus, a human surgeon must clear a path for the device to access the
[16]
bone by mobilizing the skin and fascia superficial to the bone.

Autonomous surgical systems currently employed in the OR exhibit a range of autonomous capabilities.
The varying levels of autonomy in surgical devices necessitated a classification system to identify their
operative capabilities. In 2017, Yang et al. proposed a framework outlining the stages of automation in
surgical procedures based on the taxonomy of automated self-driving vehicles [17,18] . These six stages delineate
the progression from manual to fully autonomous surgical devices, encompassing various levels of human
involvement and, conversely, machine autonomy. This framework provides a comprehensive roadmap for
understanding the evolution of surgical automation, delineating distinct stages of technological
advancement and human-machine collaboration. The stages are outlined below [Table 1].

Stage 0 - no autonomy
In this initial stage, surgeons perform procedures manually with minimal technological assistance. Surgeons
rely solely on their skills and expertise to execute surgical tasks without the aid of automation technologies.
This stage also includes teleoperated devices that respond directly to the surgeon’s command even from a
distance.

Stage 1 - robot assistance
This stage provides mechanical assistance during the procedure to aid the surgeon’s skills. Surgeons operate
consoles equipped with haptic feedback, enabling them to control robotic arms with precision while
visualizing the surgical site through advanced automated imaging modalities.

Stage 2 - task autonomy
In stage 2, the operator maintains control of the system and the robot can perform surgeon-defined tasks
autonomously. These semi-autonomous systems incorporate AI-driven algorithms to assist surgeons during
specific phases of surgery. These systems analyze intraoperative data and provide contextual guidance,
enhancing surgical precision and safety. Surgeons retain control over critical decision-making aspects while
leveraging automation for assistance.