AI in robotics spans training, preoperative planning, and simulation.

Intraoperative assistance includes real-time AR, instrument tracking, and conditional autonomy.

AI tools provide skill assessment and outcome prediction using intraoperative data.

Surgical automation progresses in stages, from augmented guidance to fully autonomous procedures.

Postoperative analytics evaluate surgical performance and predict patient outcomes.

Current robotic systems still lack tactile feedback and full automation capabilities.

AI enhances visualisation, precision, and decision-making during surgeries.

Adoption of AI requires ethical use, standardisation, and open data sharing.

AI models combine patient data with robotic sensors for better surgical planning and simulation.

Real-time augmented reality (AR) provides intraoperative assistance, helping surgeons visualise critical structures and blood flow.

Current robots function with minimal automation, primarily under the surgeon’s control (master-slave systems).

Progression in autonomy involves robots performing specific tasks independently (e.g., suturing between marked points).

Fully autonomous procedures, like emergency surgeries, are envisioned for future applications.

AI systems (e.g., DV Logger) analyse intraoperative video and instrument movements to assess performance.

Surgical gestures and instrument kinematics can predict surgeon expertise and postoperative outcomes.

AI offers real-time camera control, tissue recognition, and de-noising to improve surgical visualisation.

Tactile feedback through force-sensing algorithms is under development to replicate human touch during robotic surgeries.

AI will augment, not replace, surgeons, focusing on enhancing education, safety, and precision.

Standardised reporting and ethical guidelines are needed for responsible AI integration into surgical platforms.