Artificial Intelligence-Based Video Feedback to Improve Novice Performance on Robotic Suturing Skills: A Pilot Study

Area of Science:

• Surgical Education
• Robotic Surgery
• Artificial Intelligence in Medicine

Background:

• Automated skills assessment offers objective, personalized feedback for surgical trainees.
• Artificial intelligence (AI) can be leveraged to provide targeted feedback in surgical training.

Purpose of the Study:

• To evaluate the efficacy of AI-based feedback on robotic suturing skills.
• To determine if AI feedback impacts skill acquisition differently between underperformers and innate performers.

Main Methods:

• Forty-two novices were randomized into control and AI feedback groups for robotic suturing tasks.
• Participants completed two rounds of tasks; feedback was provided after the first round.
• Skill assessment focused on needle handling and driving, with underperformers and innate performers identified.

Main Results:

• The AI feedback group showed significantly greater improvement in needle handling compared to the control group.
• Underperformers receiving AI feedback demonstrated enhanced needle handling skills.
• No significant difference in needle driving improvement was observed between groups.

Conclusions:

• AI-based feedback is effective in improving robotic surgical trainees' technical skills, especially for those initially underperforming.
• AI feedback shows promise for enhancing skill acquisition in robotic surgery training programs.