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Artificial Intelligence in Laparoscopic Skill Assessment: A Scoping Review.
Justin Yu Hin Phung1, Jeremy King Hei Lee2, Cynthiya Gnanaseelan1
1Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.
Journal of Surgical Education
|June 5, 2026
Summary
Artificial intelligence (AI) offers an objective and scalable method for assessing laparoscopic skills in surgical education. AI effectively differentiates skill levels and automates evaluation, potentially accelerating training and reducing bias.
Area of Science:
- Surgical Education
- Medical Technology
- Artificial Intelligence
Background:
- Traditional laparoscopic skill assessment methods can be subjective and time-consuming.
- There is a growing need for objective and efficient tools in surgical training.
- Artificial intelligence (AI) presents a potential solution for enhancing skill evaluation.
Purpose of the Study:
- To synthesize existing literature on the application of AI for laparoscopic skill assessment in surgical education.
- To describe the current state of AI-based tools for evaluating surgical proficiency.
Main Methods:
- A scoping review was conducted following PRISMA-ScR guidelines.
- Literature search included MEDLINE, EMBASE, and Web of Science databases (2015-2024).
- Included studies focused on AI assessments for medical students, residents, and fellows in laparoscopic procedures.
Main Results:
- 30 studies were included, evaluating AI versus traditional scoring (40%) or AI for skill level differentiation (60%).
- AI models showed moderate to high accuracy in distinguishing novice from expert surgeons.
- AI automated skill assessment via real-time data and video analysis, with strong agreement with human experts.
Conclusions:
- AI provides an objective, scalable, and automated approach to laparoscopic skill assessment.
- Real-time AI feedback can accelerate skill acquisition and improve training efficiency.
- Standardization of AI metrics is crucial for widespread adoption in surgical education.