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Revista argentina de cirugía
versión impresa ISSN 2250-639Xversión On-line ISSN 2250-639X
Rev. argent. cir. vol.116 no.2 Cap. Fed. jun. 2024 Epub 01-Jun-2024
http://dx.doi.org/10.25132/raac.v115.n2.1782
Special article
The age of artificial intelligence and its impact on learning surgical skills: is it the future of surgery?
1
http://orcid.org/0000-0002-3794-0413
1
http://orcid.org/0000-0003-2556-1202
1Universidad del Quindío. Grupo de Interés e Investigación en Cirugía (GIICx-UQ). Armenia, Colombia.
Educational changes present a challenge for teachers in terms of how to effectively teach and enhance student performance. Surgery demands manual dexterity that reflects critical thinking and the ability to make efficient decisions quickly in complex situations. Artificial Intelligence (AI) is a tool that can enhance the performance of both undergraduate and graduate students and improve clinical outcomes. The role of traditional teaching and the future of surgical education need to be addressed.
Keywords: Artificial intelligence; surgical learning; surgical education
Teaching surgery at the undergraduate and postgraduate levels is a complex area of medical education. It involves the interaction of scientific knowledge, technical and non-technical skills, theoretical and practical guidance, and the deliberate practice of the student. The experience of the professor is also important, as they can identify critical issues in a procedure and provide tips to their students1, 2,3, 4. In this way, students can apply these tips to their individual medical practice, a delicate combination of art and science. Mentoring helps students to become professionals in the art of surgery5, 6,7, 8. This close relationship between professor and student meets academic and technical standards and involves a learning process similar to that of an apprentice learning from a craftsman. This process requires a high level of self-directed and deliberate scientific knowledge, supported by the development of motor skills with a phenomenological nuance9,10.
Surgical education aims to shorten learning curves, improve decision-making, develop critical thinking and situational awareness, and enhance technical performance, ultimately leading to improved clinical outcomes. To attain this level of expertise, trainees must acquire skills following a model similar to that proposed by Dreyfus11,12, in which the transition from novice to expert is achieved through formal training and skills in a system of deliberate practice13. Educational strategies such as simulation14, 15,16, 17 and new technologies like surgical training improved intraoperative performance, robotic surgery, and telecoaching have emerged to allow real-time monitoring of events and anticipation to rethink the surgical approach or technical alternatives in situ.
Similarly, the use of artificial intelligence (AI) has grown exponentially. Optimizing the study of algorithms has made it possible to represent and improve various human cognitive processes, such as decision-making and problem-solving18,19. The emergence of AI, computer vision, augmented reality, navigation, 3D visualization, and machine learning (ML) has led to their use as educational tools that facilitate multimodal analysis of technical, physiological, and functional performance20, 21, 22.
All of this is achieved through a relationship between surgical education, innovation and technology, without losing sight of the ultimate goal: comprehensive student training and better clinical outcomes. As a result, its benefits have been harnessed in a wide range of fields, including medicine, to the extent that some experts claim that AI is transforming modern healthcare 23, 24, 25. In the field of surgery, emerging technologies have the potential to improve the efficiency and outcomes of surgical treatments by collecting and processing large amounts of data. This information can be useful throughout the patient’s care, from initial consultation to postoperative management26.
The use of AI is useful not only for experienced surgeons but also for surgical learning. It represents a significant improvement in the way surgical skills are acquired, allowing students to develop competencies in controlled and personalized environments that adapt to individual needs even from undergraduate medical training27, 28, 29. This technology improves accuracy, efficiency, and safety in clinical practice. The Global Surgical AI Collaborative reports that surgical procedures result in 3-5% of adverse events annually, 75% of which are preventable. This is part of what was reported in the publication To Err is Human which highlights healthcare-related events as the third leading cause of death in the United States. As a result, healthcare quality and patient safety policies have been revised30. Therefore, the impact on medical education, especially surgical education in this case, is high22; and in the case of middle- and low-income countries, it is a safe alternative. Depending on the strategy, it may be useful to train students, especially given the shortage of formally trained personnel31 and limited access to practice centers.
For instance, AI allows for personalized feedback and offers an immersive surgical experience for visualizing patient’s anatomy32. It also facilitates surgical procedures of varying complexity, as well as robotic medicine33, which is currently used in high- complexity centers. Academic planning should include AI in the curriculum in a structured and comprehensive way19,34, 35, 36. This will allow students to receive synchronous and asynchronous feedback and advice in both simulated and real clinical scenarios.
This is where AI goes beyond technicality by using information collected through simulation recordings, intraoperative monitoring, and radiological images of the patient to facilitate data analysis. This provides valuable feedback and helps develop technologies for trainees in both simulated and real cases. Similarly, this information can be validated and structured for academic and clinical implementation. Inter-institutional networks can be established to reach consensus on the usefulness, applicability, and standardization of AI in transferring this data to surgical education, including evaluation and feedback between simulation and the operating room, such as the use of OSATS or the JIGSAWS database. In this way, AI can help eliminate biases in medical education37. Beyond the fears that AI may raise, it is a new tool with the ability to integrate big data, recognize patterns, and create models to overcome human limitations, reduce medical burdens, expedite care, provide more personalized treatments, and optimize resources38, 39,40, 41.
Although students are just entering the world of surgery, the combination of traditional teaching with current technological tools represents a major step forward in terms of their education and future surgical performance. AI enables accurate education and information access by utilizing thousands of databases and reliable bibliographic sources. This empowers students to answer patients’ questions and improve their confidence.
Professional training should take into account bioethical aspects related to AI, patient information, and imaging test results. It is important to maintain a balance between the use of AI and comprehensive medical education to prevent the dehumanization of medicine. In this sense, a major challenge is to incorporate professionalism, humanization, and empathy42 into AI in surgical education39,43.
Fazlollahi et al.44 conducted the first study comparing the effectiveness of AI tutoring systems with remote expert instruction and found better procedural performance during practice and on high- fidelity simulation scenarios when students were guided by an AI program. However, participants in the instructor group felt more relieved and relaxed during training compared to learners in the AI group, although there was no evidence of higher cognitive demands or poorer performance during the procedure44. This AI intervention saved approximately 53 hours of expert supervision and formative assessment compared to the instructor group. Although the results were favorable for using AI as a learning method, most students indicated a preference for learning from both AI and traditional instruction. This shows how important it is to think of AI as an additional tool that does not completely replace the traditional methods of teaching.
On the other hand, the implementation of an AI-based model for education has certain limitations. First, there is currently no consensus on the various AI-based models that have been approved for implementation in a standardized training system. Therefore, their dissemination and large-scale use remain a challenge45, 46, 47 despite a generally positive attitude toward AI among surgeons48. Second, conventional teaching methods for case analysis and knowledge transfer may outperform machine learning models based on AI49. This is further supported by the fact that the analysis of a situation is influenced by the previously available data; therefore, systematic biases may occur which can alter the robustness of a response or behavior50. Third, current AI-based models cannot assess other critical competencies, such as interdisciplinary teamwork. Finally, it is important to recognize the value of having solid technical and non- technical foundations4,51,52, as poor standardization of data analysis systems can lead to errors and have disastrous consequences38.
In conclusion, the use of AI as a tool for surgical skill acquisition has become increasingly popular in recent years. Overall, AI is a useful tool in many ways. This model provides personalized feedback and allows for the creation of strategies to improve weak areas, as well as immersion from 3D models, ease of preoperative planning, improved performance compared to traditional tutoring methods, and optimization of the time needed to achieve satisfactory results. However, it is important to emphasize that it has significant limitations, such as non-standardization, lack of superiority over traditional teaching models in certain situations, and the need to count with a large dataset on the case to be treated in order to obtain reliable and solid feedback. All these considerations indicate the necessity for additional studies to assess conventional learning and decision-making methods, as well as the influence of AI-based algorithms in surgical practice. This will lead to the development of future learning models that fully utilize this tool and mitigate any limitations or ethical implications that may arise. It is important to note that AI should serve as a tool for teaching, rather than a goal, with the teaching surgeon playing a leading role.
Acknowledgments
To GIICx-UQ students and Andrés Felipe Barrios Puerta.
REFERENCES
1. Youngson GG. Teaching and assessing non-technical skills. Surgeon [Internet]. 2011;9(Suppl. 1): S35-7. Available from: http://dx.doi.org/10.1016/j.surge.2010.11.004 [ Links ]
2. Anderson C, Gupta R, Larson J. Impact of objectively assessing surgeons' teaching on effective perioperative instructional behaviors. JAMA Surg [Internet]. 2013;148(10):915-22. http://dx.doi.org/10.1001/jamasurg.2013.2144 [ Links ]
3. Min H, Morales DR, Orgill D, Smink DS, Yule S. Systematic review of coaching to enhance surgeons' operative performance. Surgery. 2015;158(5):1168-91. [ Links ]
4. Yule S, Parker SH, Wilkinson J, McKinley A, MacDonald J, Neill A, et al. Coaching Non-technical Skills Improves Surgical Residents' Performance in a Simulated Operating Room. J Surg Educ. 2015;72(6):1124-30. [ Links ]
5. Barr J, Graffeo CS. Procedural Experience and Confidence among Graduating Medical Students. J Surg Educ [Internet]. 2016;73(3):466-73. http://dx.doi.org/10.1016/j.jsurg.2015.11.014 [ Links ]
6. Trickey AW, Newcomb AB, Porrey M, Wright J, Bayless J, Piscitani F, et al. Assessment of Surgery Residents' Interpersonal Communication Skills: Validation Evidence for the Communication Assessment Tool in a Simulation Environment. J Surg Educ [Internet]. 2016;73(6): e19-27. http://dx.doi.org/10.1016/j.jsurg.2016.04.016 [ Links ]
7. Martimianakis MA, Michalec B, Lam J, Cartmill C, Taylor JS, Hafferty FW. Humanism, the hidden curriculum, and educational reform: A scoping review and thematic analysis. Acad Med. 2015;90(11): S5-13. [ Links ]
8. O'Sullivan H, van Mook W, Fewtrell R, Wass V. Integrating professionalism into the curriculum: AMEE Guide No. 61. Med Teach. 2012; 34(2): e64-77. [ Links ]
9. Alfageme-González MB, Díaz-Serrano J. Estilos de enseñanza: punto de partida de la identidad y el desarrollo profesional docente. Roteiro. 2020; 45:1-22. [ Links ]
10. Planella J. Pedagogía y hermenéutica. Más allá de los datos en la educación. Rev Iberoam Educ [Internet]. 2001;36/12:12. http://www.rieoei.org/deloslectores/1078Planella.PDF [ Links ]
11. Tsuda S, Scott D, Doyle J, Jones DB. Surgical Skills Training and Simulation. Curr Probl Surg. 2009;46(4):271-370. [ Links ]
12. Epstein RM. Assessment in Medical Education. N Engl J Med. 2007;356:387-96. [ Links ]
13. Magas CP, Gruppen LD, Barrett M, Dedhia PH, Sandhu G. Intraoperative questioning to advance higher-order thinking. Am J Surg. 2017;213(2):222-6. [ Links ]
14. Zigmont JJ, Kappus LJ, Sudikoff SN. Theoretical Foundations of Learning through Simulation. Semin Perinatol [Internet].2011;35(2):47-51. http://dx.doi.org/10.1053/j.semperi.2011.01.002 [ Links ]
15. McAnena PF, O'Halloran N, Moloney BM, Courtney D, Waldron RM, Flaherty G, et al. Undergraduate basic surgical skills education: impact on attitudes to a career in surgery and surgical skills acquisition. Ir J Med Sci. 2018;187(2):479-84. [ Links ]
16. Kim S, Dunkin BJ, Paige JT, Eggerstedt JM, Nicholas C, Vassilliou MC, et al. What is the future of training in surgery? Needs assessment of national stakeholders. Surg (United States). 2014;156(3):707-17. [ Links ]
17. Dehabadi M, Fernando B, Berlingieri P. The use of simulation in the acquisition of laparoscopic suturing skills. Int J Surg [Internet]. 2014;12(4):258-68. Available from: http://dx.doi.org/10.1016/j.ijsu.2014.01.022 [ Links ]
18. Davenport T, Kalakota R. The Potential for Artificial Intelligence in Healthcare. Futur Healthc J. 2019;6(2):94-8. [ Links ]
19. Chan KS, Zary N. Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review. JMIR Med Educ. 2019;5(1):e13930. [ Links ]
20. Bissonnette V, Mirchi N, Ledwos N, Alsidieri G, Winkler-Schwartz A, Del Maestro RF. Artificial Intelligence Distinguishes Surgical Training Levels in a Virtual Reality Spinal Task. J Bone Jt Surg Am. 2019;00(e1):1-8. [ Links ]
21. Fazlollahi AM, Bakhaidar M, Alsayegh A, Yilmaz R, Winkler-Schwartz A, Mirchi N, et al. Effect of artificial intelligence tutoring vs expert instruction on learning simulated surgical skills among medical students a randomized clinical trial. JAMA Netw Open. 2022;5(2): e2149008. [ Links ]
22. Guerrero DT, Asaad M, Rajesh A, Hassan A, Butler CE. Advancing Surgical Education: The Use of Artificial Intelligence in Surgical Training. Am Surg. 2023;89(1):49-54. [ Links ]
23. Yu K-H, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2: 719-31. [ Links ]
24. Kavian JA, Wilkey HL, Patel PA, Boyd CJ. Harvesting the Power of Artificial Intelligence for Surgery: Uses, Implications, and Ethical Considerations. Am Surg. 2023;1-3. [ Links ]
25. Petrone P. Artificial intelligence and artificial neural networks. Rev Colomb Cir. 2023;38(3):407-12. [ Links ]
26. Pucchio A, Rathagirishnan R, Caton N, Gariscsak PJ, Del Papa J, Nabhen JJ, et al. Exploration of exposure to artificial intelligence in undergraduate medical education: a Canadian cross-sectional mixed-methods study. BMC Med Educ. 2022;22(1):815. [ Links ]
27. Liu P ran, Lu L, Zhang J yao, Huo T tong, Liu S xiang, Ye Z wei. Application of Artificial Intelligence in Medicine: An Overview. Curr Med Sci. 2021;41(6):1105-15. [ Links ]
28. Masters K. Artificial intelligence in medical education. Med Teach. 2019;41(9):976-80. [ Links ]
29. Park JJ, Tiefenbach J, Demetriades AK. The role of artificial intelligence in surgical simulation. Front Med Technol. 2022;4(December):1076755. [ Links ]
30. Kohn LT, Corrigan JM, Donaldson MS. To Err Is Human [Internet]. Vol. 126, Committee on Quality of Health Care in America; Institute of Medicine. 1999. http://www.journals.cambridge.org/abstract_S095026880100509X [ Links ]
31. Bhatia MB, Namazi B, Matthews J, Thomas C, Doster D, Martinez C, et al. Use of artificial intelligence to support surgical education personnel shortages in low-and middle-income countries: developing a safer surgeon. Glob Surg Educ-J Assoc Surg Educ [Internet]. 2023;2(1):1-8. https://doi.org/10.1007/s44186-023-00142-4 [ Links ]
32. Siyar S, Azarnoush H, Rashidi S, Del Maestro RF. Tremor Assessment during Virtual Reality Brain Tumor Resection. J Surg Educ. 2020;77(3):643-51. [ Links ]
33. Palomares Orihuela RJ. Robot Da Vinci: El quirófano del futuro. Perfiles Ing. 2016;2(11):155--62. [ Links ]
34. Crispin M. Artificial Intelligence in Surgical Education and Training. Clin Educ Health Prof. 2021;1-11. [ Links ]
35. Sun L, Yin C, Xu Q, Zhao W. Artificial intelligence for healthcare and medical education: a systematic review. Am J Transl Res. 2023;15(7):4820-8. [ Links ]
36. Bilgic E, Gorgy A, Yang A, Cwintal M, Ranjbar H, Kahla K, et al. Exploring the roles of artificial intelligence in surgical education: A scoping review. Am J Surg [Internet]. 2022;224(1):205-16. https://doi.org/10.1016/j.amjsurg.2021.11.023 [ Links ]
37. Kirubarajan A, Young D, Khan S, Crasto N, Sobel M, Sussman D. Artificial Intelligence and Surgical Education: A Systematic Scoping Review of Interventions. J Surg Educ [Internet]. 2021;79(2):500-15. Available from: https://doi.org/10.1016/j.jsurg.2021.09.012 [ Links ]
38. Álvarez Vega M, Quirós Mora LM, Cortés Badilla MV. Inteligencia artificial y aprendizaje automático en medicina. Rev Médica Sinerg. 2020;5(8):e557. [ Links ]
39. Caparrós Galán G, Sendra Portero F. Percepciones de estudiantes de Medicina sobre el impacto de la inteligencia artificial en radiología. Radiología. 2022;64(6):516-24. [ Links ]
40. Kiyasseh D, Laca J, Haque TF, Miles BJ, Wagner C, Donoho DA, et al. A multi-institutional study using artificial intelligence to provide reliable and fair feedback to surgeons. Commun Med. 2023;3(1):1-12. [ Links ]
41. Mirchi N, Bissonnette V, Yilmaz R, Ledwos N, Winkler-Schwartz A, Del Maestro RF. The virtual operative assistant: An explainable artificial intelligence tool for simulation-based training in surgery and medicine. PLoS One [Internet]. 2020;15(2):1-15. Available from: http://dx.doi.org/10.1371/journal.pone.0229596 [ Links ]
42. West E, Singer-Chang G, Ryznar R, Ross D, Czekajlo M, Hoang T, et al. The Effect of Hyper-Realistic Trauma Training on Emotional Intelligence in Second Year Military Medical Students. J Surg Educ [Internet]. 2020;77(6):1422-8. https://doi.org/10.1016/j.jsurg.2020.04.020 [ Links ]
43. Kobus V, Calletti MJ, Santander J. Prevalencia de síntomas depresivos, síntomas ansiosos y riesgo de suicidio en estudiantes de medicina de la Pontificia Universidad Católica de Chile. Rev Chil Neuropsiquiatr [Internet]. 2020;58(4):314-23 https://www.sonepsyn.cl [ Links ]
44. Austin PC, Tu J V., Lee DS. Logistic regression had superior performance compared with regression trees for predicting in-hospital mortality in patients hospitalized with heart failure. J Clin Epidemiol [Internet]. 2010;63(10):1145-55. http://dx.doi.org/10.1016/j.jclinepi.2009.12.004 [ Links ]
45. Buckley CE, Kavanagh DO, Traynor O, Neary PC. Is the skillset obtained in surgical simulation transferable to the operating theatre? Am J Surg [Internet]. 2014;207(1):146-57. Available from: http://dx.doi.org/10.1016/j.amjsurg.2013.06.017 [ Links ]
46. Traynor OJ, O'Connell PR. The Past, Present and Future of Surgical Education in Ireland. Indian J Surg [Internet]. 2022;84(Suppl 1):S147-52. Available from: https://doi.org/10.1007/s12262-021-02988-8. [ Links ]
47. Dumon KR, Traynor O, Broos P, Gruwez JA, Darzi AW, Williams NN. Surgical education in the new millennium: the European perspective. Surg Clin North Am. 2004; 84: 1471-91. [ Links ]
48. Voskens FJ, Abbing JR, Ruys AT, Ruurda JP, Broeders IAMJ. A nationwide survey on the perceptions of general surgeons on artificial intelligence. Artif Intell Surg. 2022;2: 8-17. [ Links ]
49. Pakkasjärvi N, Luthra T, Anand S. Artificial Intelligence in Surgical Learning. Surgeries (Switzerland). 2023;4(1):86?-97. [ Links ]
50. Capelli G, Verdi D, Frigerio I, Rashidian N, Ficorilli A, et al. White paper: ethics and trustworthiness of artificial intelligence in clinical surgery. Artif Intell Surg. 2023;3(2):111-22. [ Links ]
51. Navarro F, González S, Gabrielli M. Evaluación de las habilidades no técnicas en cirugía. Rev Cir (Mex). 2019;71(4):359-65. [ Links ]
52. Alvarado IJ, Henríquez RJP, Castillo RR, Sosa BJ, León FF, Varas CJ, et al. Programa Pionero de Simulación en Sutura para Estudiantes de Medicina de Pregrado. Rev Chil Cirugía. 2015;67(5):480-5. [ Links ]
Received: June 23, 2022; Accepted: January 18, 2023
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