The integration of artificial intelligence with robotic instruments in surgical practice

We are on the verge of a new era of surgery, Surgery 4.0, characterized by tele-surgery, remotely assisted operating rooms, and real-time robotic navigation. This innovation is driven by digitization, big data and informatization, fuelling the development of surgical applications, from preoperative planning to intraoperative assistance and robotic-assisted surgery1. Artificial intelligence (AI) is central to these developments, integrating with robotics to create intelligent surgical robotic platforms. The primary goal is to improve patient outcomes by reducing human error and increasing precision. Meanwhile, the ambition of autonomous surgical robotics may also be close to realization; the power of AI is being harnessed to drive autonomy through machine learning, skill analysis and skill automation. Machine learning can be achieved in various ways: through surgical video learning, imitation, programming or simulators2. A surgical robot can now learn simple tasks like suturing and knotting by recording and processing videos of different surgeons performing the same exercise with the same robotic system. This has been achieved through collaboration between researchers at the University of California, Berkeley, Intel and Google AI, resulting in the development of the algorithm Motion2Vec. This algorithm plays a key role in teaching the robot by analysing the videos and learning from ‘real’ surgeons’ techniques3. Meanwhile, in 2016 the STAR robot (Smart Tissue Autonomous Robot) successfully completed an autonomous intestinal primary suture on a pig model. Although it still required human supervision and assistance in guiding the robot, this was an exciting milestone in progress towards surgical autonomy4. AI enables bespoke patient-specific planning in robotic surgery by integrating cross-sectional imaging data to accurately map the surgical field. AI systems can also process real-time data from cameras, sensors and imaging devices to assist with surgical navigation. This includes highlighting critical structures, detecting changes in patient condition and adjusting surgical approaches and plans in real time. A number of AI applications support technology that is already in use, including in robotic stapling devices, eye tracking technology and haptic feedback or force sensing. The feedback system within robotic stapling devices allows the robotic system to identify the optimal pressure needed to compress tissues prior to staple firing (Fig. 1). By analysing tissue characteristics and the force applied, the system can automatically adjust and optimize the stapling process. If the compression is insufficient, the robot provides additional time and pressure to ensure proper closure (see video). This AI-driven feedback loop improves the precision and safety of surgical staples and enhances patient outcomes by minimizing potential complications. Intraoperative view of a robotic stapler SureForm® (Intuitive Surgical, Inc., Sunnyvale, CA) Source: Created by the author. Eye-tracking technology follows the surgeon’s gaze and can automatically adjust the camera accordingly. The camera is focused on the surgical field that the surgeon is looking towards, akin to having an expert camera-holder for a laparoscopic procedure (Fig. 2). This hands-free control enhances efficiency, allowing the surgeon to maintain focus on the procedure without any interruptions and reducing the cognitive workload. There are a variety of eye-tracking devices commercially available, each offering different capabilities for various applications, including robotic surgery. A recent systematic review included 26 different eye-tracking devices, reflecting the broad range of options for surgeons5. Eye tracking is emerging as a valuable and promising tool in surgery, with ongoing development poised to bring further advancements. An eye-gaze tracking integrated into the surgical system6 a The eye gaze is focused at the top of the image. b The gaze is focused on the bottom of the image. c The camera zooms to a defined area of the image. Lack of haptic feedback has been stated as a major limitation of robotic surgical systems, risking both on- and off-screen injury. However, the newest robotic systems now feature haptic feedback technology, offering real-time information to surgeons about the pressure they are applying to a tissue (Fig. 3). This technology helps avoid tissue damage and improves the surgeon’s ability to perform delicate surgical tasks. This represents a major shift from the reliance on visual feedback, as it enables surgeons to feel the force they are exerting, similar to open surgery. The haptic feedback can be set to provide low, medium or high feedback intensity, depending on surgeon preference and procedure. Da Vinci force sense system7 High-definition cross-sectional imaging can aid in the preoperative planning of complex surgical procedures, but until recently was not translated into real-time intraoperative guidance. The integration of real-time navigation in robotic surgery aims to improve surgical precision, safety and patient outcomes. AI guidance and robotic systems are already used in complex heart and lung surgeries to enhance precision and reduce recovery times. In orthopaedic surgery, they assist in planning and executing joint replacements with high accuracy8, whereas in neurosurgery, they minimize risk of damage to critical cranial and spinal anatomy9. Within colorectal surgery, AI navigation has recently been used during a transanal total mesorectal excision (TATME) procedure10, allowing the surgeon’s location to be displayed in real-time on superimposed MRI scans. This system provides precise guidance and enhances surgical safety by emitting a flashing light when the surgeon is near critical structures, for example the prostate. Future developments in surgical navigation will aim to further decrease morbidity rates from inadvertent injury to adjacent structures4. The described evolution, Surgery 4.0, is bringing exciting new technologies into today’s operating theatres, particularly in the domain of robotic surgical instruments and the evolving robotic platforms. These advanced tools are reshaping the landscape of surgery, already offering innovations such as robotic staplers, eye-tracking technology and haptic feedback. These developments promise to enhance precision, safety and efficiency, allowing surgeons to perform procedures with ever greater control and accuracy. Autonomous surgical robots operating on patients without human supervision or guidance may feel a significant way off, but progress continues in this direction. Of course, the ethics and patient acceptance of such an approach may yet provide a significant barrier to the routine use of such exciting technologies. Patricia Tejedor (Methodology, Validation, Writing—original draft, Writing—review & editing), and Quentin Denost (Investigation, Methodology, Supervision, Validation, Writing—original draft, Writing—review & editing) The authors have no funding to declare. The authors declare no conflict of interest. All procedures performed in studies involving humans were in accordance with ethical standards of the institutional research committee and the 1964 Helsinki declaration and its later amendments. No new data were generated or analysed in support of this research.