Developing an intelligent tutoring system for robotic‐assisted surgery instruction
Abstract
Background: Robotic‐assisted laparoscopic surgery (RALS) is an operative innovation that has sparked global interest. Over the last decade, RALS cases have rapidly increased with over 750 000 robotic procedures completed in 2017. Until recently, Intuitive's da Vinci surgical system has been the only Food and Drug Administration (FDA)–approved robotic‐assisted surgical device for human procedures. Robotic procedures with the da Vinci require a specific, dedicated training due to the introduction of the technological components and psychomotor skills needed to successfully utilize this system. When a surgeon becomes interested in learning robotics, there are limited avenues for training. Surgeons typically receive instruction on the necessary psychomotor and Operation Room (OR) communication skills in isolation from the cognitive and perceptual skills and may only perform these skills in an integrated manner during a 1‐ or 2‐day course. Methods: This paper discusses the development of a computer‐based intelligent tutoring system (ITS) to train the cognitive and procedural skills needed to complete basic robotic suturing to novice robotic surgeons. The system was developed using the generalized intelligent framework for tutoring framework of tools. This information was captured as video, instruction sets, and flowcharts, which were reviewed for accuracy by surgeons and then encoded into an ITS using the generalized intelligent framework for tutoring tools. Conclusion: The purpose of this paper was threefold—(a) explain the process used to obtain the critical data behind a basic robotic task, (b) develop an entry‐level ITS to train the cognitive process and procedural steps behind multiple fundamental robotic surgery skills, and (c) provide future novice ITS developers lessons learned and future recommendations beyond the initial ITS prototype.
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AI digital tutors provide real-time feedback and adaptive learning support, which are difficult to achieve in traditional classroom settings.
“First, AI digital tutors provide real-time feedback and adaptive learning support, which are difficult to achieve in traditional classroom settings [28, 33].”
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- Authors
- Julian, D., & Smith, R.
- Affiliations
- AdventHealth Celebration (US)
- Corresponding author
- Julian, Danielle
- Venue
- The International Journal of Medical Robotics and Computer Assisted Surgery
- Publisher
- Wiley
- Published
- 2019-11-18
- Volume
- 15
- Issue
- 6
- Pages
- e2037-e2037
- ISSN
- 1478-5951 (ISSN-L)
- 1478-596X
- OpenAlex
- W2973037943
- Cited by
- 19
- References
- 19
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 3 · 2022: 3 · 2021: 5 · 2020: 2
- FWCI
- 2.39
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Medical Robotics and Computer Assisted Surgery (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Surgery › Surgical Simulation and Training
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Keywords
- intelligent tutoring systems, robot-assisted laparoscopic surgery, surgical training, da Vinci surgical system, robotic suturing, cognitive skills, psychomotor skills, surgical skill acquisition, computer-based training
- MeSH terms
- Communication, Computer Graphics, Curriculum, Education, Medical, Graduate, Humans, Inservice Training, Operating Rooms, Laparoscopy, Psychomotor Performance, Suture Techniques, United States, User-Computer Interface, Video Recording, Robotic Surgical Procedures
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