Technical Skills Assessment in Robotic Surgery: A Review of Recent Methods

Area of Science:

• Robotics in Surgery
• Surgical Education
• Medical Technology Assessment

Background:

• Robot-assisted minimally invasive surgery (RAMIS) is increasingly prevalent, necessitating effective skill assessment.
• Traditional expert observation using rubrics is resource-intensive and prone to bias.
• There is a growing need for objective and robust methods to evaluate robotic surgical proficiency.

Purpose of the Study:

• To review recent advancements in assessing technical skills for robotic surgery.
• To focus on assessment methods used with the da Vinci surgical platform.
• To identify trends and future directions in robotic surgical skill evaluation.

Main Methods:

• Categorization of assessment methods into structured rubrics, skill-based metrics, crowd-sourcing, and automated models.
• Review of studies focusing on the da Vinci surgical system.
• Analysis of trends in rubric adaptation, deep learning implementation, and crowd-sourcing integration.

Main Results:

• Established rubrics are being adapted for specialty areas.
• Deep learning models show promise for automated skill assessment.
• Crowd-sourcing platforms are being explored for efficient evaluation.
• Multilevel assessment strategies and objective feedback systems are gaining traction.

Conclusions:

• While traditional rubrics persist, a shift towards data-driven, objective assessment is evident.
• Automated and crowd-sourced methods offer potential for more efficient and unbiased skill evaluation.
• Integrating task- and movement-based assessments could enhance generalizability and robustness of future evaluation models.
• Improved assessment methods are expected to enhance surgical training and patient outcomes.