Aplicaciones de la inteligencia artificial en la educación quirúrgica de posgrado: revisión sistemática
DOI:
https://doi.org/10.55204/trc.v6i1.e670Palabras clave:
Inteligencia artificial; educación quirúrgica; formación médica; simulación; aprendizaje automático.Resumen
Objetivo: Analizar la evidencia científica disponible sobre el uso de la inteligencia artificial (IA) en la educación quirúrgica de posgrado.
Metodología: Se realizó una revisión sistemática siguiendo estándares metodológicos internacionales. La búsqueda se efectuó en PubMed, Scopus, Web of Science y Embase, incluyendo estudios publicados entre 2015 y 2025. Se incluyeron ensayos clínicos aleatorizados, estudios experimentales y de validación tecnológica enfocados en médicos en formación de posgrado.
Resultados: Se seleccionaron 29 estudios. La evidencia muestra que la IA mejora significativamente el desempeño técnico quirúrgico, reduce errores y optimiza el tiempo de aprendizaje mediante retroalimentación automatizada y evaluación objetiva. Además, permite la personalización del entrenamiento y contribuye a la estandarización de competencias. Sin embargo, se identificó heterogeneidad metodológica, tamaños muestrales limitados y escasa validación externa.
Conclusiones: La inteligencia artificial representa una herramienta innovadora con alto potencial para mejorar la formación quirúrgica de posgrado. No obstante, su implementación debe ser progresiva, complementaria a la enseñanza tradicional y basada en evidencia sólida.
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Referencias
1. Yilmaz R, Bakhaidar M, Alsayegh A, Abou Hamdan N, Fazlollahi AM, Tee T, et al. Real-Time multifaceted artificial intelligence vs In-Person instruction in teaching surgical technical skills: a randomized controlled trial. Sci Rep. 2024. URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC11219907/
2. Ma R, Kiyasseh D, Laca JA, et al. Artificial Intelligence-Based Video Feedback to Improve Novice Performance on Robotic Suturing Skills: A Pilot Study. J Endourol. 2024;38(8):884–891. DOI: https://doi.org/10.1089/end.2023.0328. URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC11947633/
3. Trujillo CJ, Vela Ulloa J, Escalona Vivas G, et al. Surgeons vs ChatGPT: Assessment and Feedback Performance Based on Real Surgical Scenarios. J Surg Educ. 2024;81(7):960–966. DOI: https://doi.org/10.1016/j.jsurg.2024.03.012. URL: https://pubmed.ncbi.nlm.nih.gov/38749814/
4. Chan KS, Zary N. Applications of artificial intelligence in surgical simulation. Surg Innov. 2022;29(3):321–329.
Disponible en: https://pubmed.ncbi.nlm.nih.gov/34962116/
5. Frank JR, Mungroo R, Ahmad Y, et al. Toward a definition of competency-based education in medicine: a systematic review of published definitions. Med Teach. 2010;32(8):631–637. URL: https://pubmed.ncbi.nlm.nih.gov/20662573/
6. Hattie J, Timperley H. The Power of Feedback. Rev Educ Res. 2007;77(1):81–112. DOI: https://doi.org/10.3102/003465430298487. URL: https://journals.sagepub.com/doi/10.3102/003465430298487
7. Martin JA, Regehr G, Reznick R, et al. Objective structured assessment of technical skill (OSATS) for surgical residents. Br J Surg. 1997;84(2):273–278. URL: https://pubmed.ncbi.nlm.nih.gov/9052454/
8. Ahmidi N, Tao L, Sefati S, et al. A dataset and benchmarks for segmentation and recognition of gestures in robotic surgery. IEEE Trans Biomed Eng. 2017;64(9):2025–2041. DOI: https://doi.org/10.1109/TBME.2016.2647680. URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC5559351/
9. Lefor AK, Harada K, Dosis A, Mitsuishi M. Motion analysis of the JHU-ISI Gesture and Skill Assessment Working Set using Robotics Video and Motion Assessment Software. Int J Comput Assist Radiol Surg. 2020 Dec;15(12):2017-2025. DOI: 10.1007/s11548-020-02259-z.
10. Gao Y, Vedula SS, Reiley CE, et al. JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS): A surgical activity dataset for human motion modeling. MICCAI Workshop M2CAI. 2014. URL: https://cirl.lcsr.jhu.edu/wp-content/uploads/2015/11/JIGSAWS.pdf
11. Ward TM, Mascagni P, Ban Y, et al. Computer vision in surgery. J Surg Educ. 2021;78(2):325–332.
Disponible en: https://pubmed.ncbi.nlm.nih.gov/33144155/
12. Goldenberg MG. Surgical artificial intelligence in urology: educational applications. Urol Clin North Am. 2024;51(1):105–115. Disponible en: https://doi.org/10.1016/j.ucl.2023.06.003
13. 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. 2022;79(2):500–515. Disponible en: https://pubmed.ncbi.nlm.nih.gov/34756807/
14. Wu S, Tang M, Wang X, et al. Impact of an AI-based laparoscopic cholecystectomy coaching program on the surgical performance: a randomized controlled trial. Int J Surg. 2024. DOI: https://doi.org/10.1097/JS9.0000000000001798. URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC11634122/
15. Yilmaz R., Alsayegh A., Bakhaidar M. et al. Combining real-time AI and in-person expert instruction in simulated surgical skills training - Randomized crossover trial. npj Artif. Intell. 1, 36 (2025). https://doi.org/10.1038/s44387-025-00032-8
16. 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. 2020;15(2):e0229596.
Disponible en: https://doi.org/10.1371/journal.pone.0229596
17. Yilmaz R, Winkler-Schwartz A, Mirchi N, Reich A, Christie S, Tran DH, et al. Continuous monitoring of surgical bimanual expertise using deep neural networks in virtual reality simulation. npj Digit Med. 2022;5(1):54.
Disponible en: https://doi.org/10.1038/s41746-022-00596-8
18. Igaki T, et al. Automatic Surgical Skill Assessment System Based on Deep Learning. JAMA Surg. 2023. URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC10248810/
19. Kitaguchi D, Takeshita N, Matsuzaki H, Igaki T, Hasegawa H, Ito M. Development and Validation of a 3-Dimensional Convolutional Neural Network for Automatic Surgical Skill Assessment Based on Spatiotemporal Video Analysis. JAMA Netw Open. 2021;4(8):e2120786. doi:10.1001/jamanetworkopen.2021.20786
20. Lavanchy J L., Zindel J., Kirtac K. et al. Automation of surgical skill assessment using a three-stage machine learning algorithm. Sci Rep 11, 5197 (2021). https://doi.org/10.1038/s41598-021-84295-6
21. Khalid S, Goldenberg M, Grantcharov T, Taati B, Rudzicz F. Evaluation of Deep Learning Models for Identifying Surgical Actions and Measuring Performance. JAMA Netw Open. 2020 Mar 2;3(3):e201664. doi: 10.1001/jamanetworkopen.2020.1664. PMID: 32227178; PMCID: PMC12124734.
22. Funke I, Mees ST, Weitz J, Speidel S. Video-based surgical skill assessment using 3D convolutional neural networks. Int J Comput Assist Radiol Surg. 2019 Jul;14(7):1217-1225. doi: 10.1007/s11548-019-01995-1. Epub 2019 May 18. PMID: 31104257.
23. Hung AJ, Chen J, Che Z, Nilanon T, Jarc A, Titus M, et al. Utilizing machine learning and automated performance metrics to evaluate robot-assisted radical prostatectomy performance and predict outcomes. J Endourol. 2018;32(5):438–444. DOI: https://doi.org/10.1089/end.2018.0035
24. Chen J, Cheng N, Cacciamani G, Oh P, Lin-Brande M, Remulla D, Gill IS, Hung AJ. Objective assessment of robotic surgical technical skill: a systematic review. J Urol. 2019;201(3):461–469.
Disponible en: https://pubmed.ncbi.nlm.nih.gov/30053510/
25. Fard, M. J., Ameri, S., Darin Ellis, R., Chinnam, R. B., Pandya, A. K., & Klein, M. D. (2018). Automated robot-assisted surgical skill evaluation: Predictive analytics approach. The international journal of medical robotics + computer assisted surgery : MRCAS, 14(1), 10.1002/rcs.1850. https://doi.org/10.1002/rcs.1850
26. Zia, A., & Essa, I. (2018). Automated surgical skill assessment in RMIS training. International journal of computer assisted radiology and surgery, 13(5), 731–739. https://doi.org/10.1007/s11548-018-1735-5
27. Power, D., Burke, C., Madden, M.G. et al. Automated assessment of simulated laparoscopic surgical skill performance using deep learning. Sci Rep 15, 13591 (2025). https://doi.org/10.1038/s41598-025-96336-5
28. Pan, M., Wang, S., Li, J., Li, J., Yang, X., & Liang, K. (2023). An Automated Skill Assessment Framework Based on Visual Motion Signals and a Deep Neural Network in Robot-Assisted Minimally Invasive Surgery. Sensors, 23(9), 4496. https://doi.org/10.3390/s23094496
29. Ebina, K., Abe, T., Hotta, K., Higuchi, M., Furumido, J., Iwahara, N., Kon, M., Miyaji, K., Shibuya, S., Lingbo, Y., Komizunai, S., Kurashima, Y., Kikuchi, H., Matsumoto, R., Osawa, T., Murai, S., Tsujita, T., Sase, K., Chen, X., Konno, A., Shinohara, N. (2022). Automatic assessment of laparoscopic surgical skill competence based on motion metrics. PloS one, 17(11), e0277105. https://doi.org/10.1371/journal.pone.0277105
30. Goldbraikh, A., D'Angelo, A. L., Pugh, C. M., & Laufer, S. (2022). Video-based fully automatic assessment of open surgery suturing skills. International journal of computer assisted radiology and surgery, 17(3), 437–448. https://doi.org/10.1007/s11548-022-02559-6
31. Nakajima, K., Kitaguchi, D., Takenaka, S. et al. Automated surgical skill assessment in colorectal surgery using a deep learning-based surgical phase recognition model. Surg Endosc 38, 6347–6355 (2024). https://doi.org/10.1007/s00464-024-11208-9
32. Yanagida, Y., Takenaka, S., Kitaguchi, D., Hamano, S., Tanaka, A., Mitarai, H., Suzuki, R., Sasaki, K., Takeshita, N., Ishimaru, T., Fujishiro, J., & Ito, M. (2025). Surgical skill assessment using an AI-based surgical phase recognition model for laparoscopic cholecystectomy. Surgical endoscopy, 39(8), 5018–5026. https://doi.org/10.1007/s00464-025-11903-1
33. Komatsu, M., Kitaguchi, D., Yura, M., Takeshita, N., Yoshida, M., Yamaguchi, M., Kondo, H., Kinoshita, T., & Ito, M. (2024). Automatic surgical phase recognition-based skill assessment in laparoscopic distal gastrectomy using multicenter videos. Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association, 27(1), 187–196. https://doi.org/10.1007/s10120-023-01450-w
34. Jin, A., Yeung, S., Jopling, J., Krause, J., Azagury, D., Milstein, A., & Fei-Fei, L. (2018). Tool detection and operative skill assessment in surgical videos using region-based convolutional neural networks. arXiv. https://doi.org/10.48550/arXiv.1802.08774
35. Ahmidi, N., Tao, L., Sefati, S., Gao, Y., Lea, C., Haro, B. B., Zappella, L., Khudanpur, S., Vidal, R., & Hager, G. D. (2017). A Dataset and Benchmarks for Segmentation and Recognition of Gestures in Robotic Surgery. IEEE transactions on bio-medical engineering, 64(9), 2025–2041. https://doi.org/10.1109/TBME.2016.2647680
36. Gao, Y., Vedula, S. S., Reiley, C. E., Ahmidi, N., Varadarajan, B., Lin, H. C., Tao, L., Zappella, L., Béjar, B., Yuh, D. D., Chen, C. C. G., Vidal, R., Khudanpur, S., & Hager, G. D. (2014). The JHU-ISI gesture and skill assessment working set (JIGSAWS): A surgical activity dataset for human motion modeling. In Modeling and Monitoring of Computer Assisted Interventions (M2CAI) – MICCAI Workshop.
https://cirl.lcsr.jhu.edu/wp-content/uploads/2015/11/JIGSAWS.pdf
37. Yanik, E., Kruger, U., Intes, X. et al. Video-based formative and summative assessment of surgical tasks using deep learning. Sci Rep 13, 1038 (2023). https://doi.org/10.1038/s41598-022-26367-9
38. Tran, C. G., Chang, J., Sherman, S. K., & De Andrade, J. P. (2024). Performance of ChatGPT on American Board of Surgery In-Training Examination Preparation Questions. The Journal of surgical research, 299, 329–335. https://doi.org/10.1016/j.jss.2024.04.060
39. Rezaei, A., Ahmadi, M. J., Molaei, A., & Hamid, H. (2023). Video-based surgical skill assessment using tree-based Gaussian process classifier. arXiv. https://doi.org/10.48550/arXiv.2312.10208
40. Loftus TJ, Tighe PJ, Filiberto AC, et al. Artificial intelligence and surgical decision-making. Ann Surg. 2020;272(1):70–80.
Disponible en: https://pubmed.ncbi.nlm.nih.gov/32097206/
41. Senders JT, Staples PC, Karhade AV, et al. Machine learning in neurosurgery. Neurosurgery. 2018;83(2):181–192.
Disponible en: https://pubmed.ncbi.nlm.nih.gov/29506006/
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Derechos de autor 2026 Johanna Yimabel Pazmiño Figueroa, Maria Eugenia Moreta Segura, Wellington Isaac Maliza Cruz
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