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Varas J, Coronel BV, Villagrán I, Escalona G, Hernandez R, Schuit G, Durán V, Lagos-Villaseca A, Jarry C, Neyem A, Achurra P. Innovations in surgical training: exploring the role of artificial intelligence and large language models (LLM). Rev Col Bras Cir 2023; 50:e20233605. [PMID: 37646729 PMCID: PMC10508667 DOI: 10.1590/0100-6991e-20233605-en] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 07/14/2023] [Indexed: 09/01/2023] Open
Abstract
The landscape of surgical training is rapidly evolving with the advent of artificial intelligence (AI) and its integration into education and simulation. This manuscript aims to explore the potential applications and benefits of AI-assisted surgical training, particularly the use of large language models (LLMs), in enhancing communication, personalizing feedback, and promoting skill development. We discuss the advancements in simulation-based training, AI-driven assessment tools, video-based assessment systems, virtual reality (VR) and augmented reality (AR) platforms, and the potential role of LLMs in the transcription, translation, and summarization of feedback. Despite the promising opportunities presented by AI integration, several challenges must be addressed, including accuracy and reliability, ethical and privacy concerns, bias in AI models, integration with existing training systems, and training and adoption of AI-assisted tools. By proactively addressing these challenges and harnessing the potential of AI, the future of surgical training may be reshaped to provide a more comprehensive, safe, and effective learning experience for trainees, ultimately leading to better patient outcomes. .
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Affiliation(s)
- Julian Varas
- - Pontificia Universidad Católica de Chile, Experimental Surgery and Simulation Center, Department of Digestive Surgery - Santiago - Región Metropolitana - Chile
| | - Brandon Valencia Coronel
- - Pontificia Universidad Católica de Chile, Experimental Surgery and Simulation Center, Department of Digestive Surgery - Santiago - Región Metropolitana - Chile
| | - Ignacio Villagrán
- - Pontificia Universidad Católica de Chile, Carrera de Kinesiología, Departamento de Ciencias de la Salud, Facultad de Medicina - Santiago - Región Metropolitana - Chile
| | - Gabriel Escalona
- - Pontificia Universidad Católica de Chile, Experimental Surgery and Simulation Center, Department of Digestive Surgery - Santiago - Región Metropolitana - Chile
| | - Rocio Hernandez
- - Pontificia Universidad Católica de Chile, Computer Science Department, School of Engineering - Santiago - Región Metropolitana - Chile
| | - Gregory Schuit
- - Pontificia Universidad Católica de Chile, Computer Science Department, School of Engineering - Santiago - Región Metropolitana - Chile
| | - Valentina Durán
- - Pontificia Universidad Católica de Chile, Experimental Surgery and Simulation Center, Department of Digestive Surgery - Santiago - Región Metropolitana - Chile
| | - Antonia Lagos-Villaseca
- - Pontificia Universidad Católica de Chile, Department of Otolaryngology - Santiago - Región Metropolitana - Chile
| | - Cristian Jarry
- - Pontificia Universidad Católica de Chile, Experimental Surgery and Simulation Center, Department of Digestive Surgery - Santiago - Región Metropolitana - Chile
| | - Andres Neyem
- - Pontificia Universidad Católica de Chile, Computer Science Department, School of Engineering - Santiago - Región Metropolitana - Chile
| | - Pablo Achurra
- - Pontificia Universidad Católica de Chile, Experimental Surgery and Simulation Center, Department of Digestive Surgery - Santiago - Región Metropolitana - Chile
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Abstract
The technology of artificial intelligence (AI) has made significant in-roads into the field of medicine over the last decade. With surgery being a discipline where repetition is the key to mastery, the scope of AI presents enormous potential for resident education through the analysis of technique and delivery of structured feedback for performance improvement. In an era marred by a raging pandemic that has decreased exposure and opportunity, AI offers an attractive solution towards improving operating room efficiency, safe patient care in the hands of supervised residents and can ultimately culminate in reduced health care costs. Through this article, we elucidate the current adoption of the artificial intelligence technology and its prospects for advancing surgical education.
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Affiliation(s)
- David T Guerrero
- 12317University of Pittsburgh Medical School, Pittsburgh, PA, USA
| | - Malke Asaad
- Department of Plastic Surgery, 6595University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Aashish Rajesh
- 14742University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Abbas Hassan
- Department of Plastic Surgery, 571198The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Charles E Butler
- Department of Plastic Surgery, 571198The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Bilgic E, Gorgy A, Yang A, Cwintal M, Ranjbar H, Kahla K, Reddy D, Li K, Ozturk H, Zimmermann E, Quaiattini A, Abbasgholizadeh-Rahimi S, Poenaru D, Harley JM. Exploring the roles of artificial intelligence in surgical education: A scoping review. Am J Surg 2021; 224:205-216. [PMID: 34865736 DOI: 10.1016/j.amjsurg.2021.11.023] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 01/02/2023]
Abstract
BACKGROUND Technology-enhanced teaching and learning, including Artificial Intelligence (AI) applications, has started to evolve in surgical education. Hence, the purpose of this scoping review is to explore the current and future roles of AI in surgical education. METHODS Nine bibliographic databases were searched from January 2010 to January 2021. Full-text articles were included if they focused on AI in surgical education. RESULTS Out of 14,008 unique sources of evidence, 93 were included. Out of 93, 84 were conducted in the simulation setting, and 89 targeted technical skills. Fifty-six studies focused on skills assessment/classification, and 36 used multiple AI techniques. Also, increasing sample size, having balanced data, and using AI to provide feedback were major future directions mentioned by authors. CONCLUSIONS AI can help optimize the education of trainees and our results can help educators and researchers identify areas that need further investigation.
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Affiliation(s)
- Elif Bilgic
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Andrew Gorgy
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Alison Yang
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Michelle Cwintal
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Hamed Ranjbar
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Kalin Kahla
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Dheeksha Reddy
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Kexin Li
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Helin Ozturk
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Eric Zimmermann
- Department of Surgery, McGill University, Montreal, Quebec, Canada
| | - Andrea Quaiattini
- Schulich Library of Physical Sciences, Life Sciences, and Engineering, McGill University, Canada; Institute of Health Sciences Education, McGill University, Montreal, Quebec, Canada
| | - Samira Abbasgholizadeh-Rahimi
- Department of Family Medicine, McGill University, Montreal, Quebec, Canada; Department of Electrical and Computer Engineering, McGill University, Montreal, Canada; Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Canada; Mila Quebec AI Institute, Montreal, Canada
| | - Dan Poenaru
- Institute of Health Sciences Education, McGill University, Montreal, Quebec, Canada; Department of Pediatric Surgery, McGill University, Canada
| | - Jason M Harley
- Department of Surgery, McGill University, Montreal, Quebec, Canada; Institute of Health Sciences Education, McGill University, Montreal, Quebec, Canada; Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada; Steinberg Centre for Simulation and Interactive Learning, McGill University, Montreal, Quebec, Canada.
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