Artificial intelligence in the Otorhinolaryngology class

Authors

DOI:

https://doi.org/10.56294/mw2025160

Keywords:

Students, Otorhinolaryngology, Medicine, Artificial Intelligence, Universities

Abstract

Introduction: The development of curricular changes for the training of future doctors in the use of Artificial Intelligence includes the perception that medical students have about this technology and medical students are not always familiar with Artificial Intelligence. Objective: to analyze the academic performance between two groups of students who took the Otorhinolaryngology subject in the sixth year of the medical degree, one that was exposed to Artificial Intelligence and the other to the traditional method, National Autonomous University of Honduras, 2023. 
Methods: quantitative, correlational study, sample of 34 students, through non-probabilistic convenience sampling, statistical analysis was carried out with SPSS version 25 doing descriptive analysis of central tendency, grouping, and inferential with p value = 0.05 using Pearson's R test.
Results: 15 (44.1%) of the students had been studying medicine for 5 years; before taking the class, 2 (5.9%) of the students did not know what the Otorhinolaryngology specialty consisted of; There is a correlation between the curricular approach and the methodology of the subject, so that the student can learn, with a p value of 0.006 (0.005-0.008); there is a correlation between the level of knowledge acquired in Otorhinolaryngology and the methodology of the subject, p value of 0.042 (0.038-0.046). 
Conclusions: There is a significant correlation between the curricular approach and the methodology of the subject, which directly influences the student's learning process. Similarly, the level of knowledge acquired in Otorhinolaryngology is closely related to the methodology used in its teaching. In the current context, Artificial Intelligence does not replace traditional teaching given by a professor in the Medicine degree and even less so in clinical areas such as Otorhinolaryngology, where practical experience, direct observation and interaction with the patient are essential for the development of medical skills. However, it is undeniable that AI has become a valuable complementary tool, optimizing access to information, facilitating self-directed learning and offering interactive resources that enrich theoretical understanding. In addition, it allows teaching to be personalized according to different learning styles, improving knowledge retention and the overall educational experience.

References

Durning SJ, Jung E, Kim D-H, Lee Y-M. Teaching clinical reasoning: principles from the literature to help improve instruction from the classroom to the bedside. Korean J Med Educ [Internet]. 2024;36(2):145–55. Available at: http://dx.doi.org/10.3946/kjme.2024.292

Civaner MM, Uncu Y, Bulut F, Chalil EG, Tatli A. Artificial intelligence in medical education: a cross-sectional needs assessment. BMC Med Educ [Internet]. 2022;22(1):772.Available at: http://dx.doi.org/10.1186/s12909-022-03852-3

Lang J, Repp H. Artificial intelligence in medical education and the meaning of interaction with natural intelligence – an interdisciplinary approach. 2020; Available at: http://dx.doi.org/10.3205/ZMA001352

Carías A, Auza-Santivañez JC, Carias P, Condori Villca N, Vallejos-Rejas DRE, Velez Alejo RW, et al. Knowledge of research and scientific publication in medical students. Salud, Ciencia y Tecnología - Serie de Conferencias [Internet]. 2025;4:689. Disponible en: http://dx.doi.org/10.56294/sctconf2025689

Narayanan S, Ramakrishnan R, Durairaj E, Das A. Artificial intelligence revolutionizing the field of medical education. Cureus [Internet]. 2023; Available at: http://dx.doi.org/10.7759/cureus.49604

Aldaz G, Puria S, Leifer LJ. Smartphone-based system for learning and inferring hearing aid settings. J Am Acad Audiol [Internet]. 2016;27(09):732–49. Available at: http://dx.doi.org/10.3766/jaaa.15099

Khan MA, Kwon S, Choo J, Hong SM, Kang SH, Park I-H, et al. Automatic detection of tympanic membrane and middle ear infection from oto-endoscopic images via convolutional neural networks. Neural Netw [Internet]. 2020;126:384–94. Available at: http://dx.doi.org/10.1016/j.neunet.2020.03.023

Myburgh HC, Jose S, Swanepoel DW, Laurent C. Towards low cost automated smartphone- and cloud-based otitis media diagnosis. Biomed Signal Process Control [Internet]. 2018;39:34–52. Available at: http://dx.doi.org/10.1016/j.bspc.2017.07.015

Acosta Herrería DL, Santana Pérez JL, Sosa Remón A, Auza-Santivañez JC, Jeréz Alvarez AE, Santana León JL, et al. Artificial Intelligence and Medicine: Where is scientific and technical development taking us? Seminars in Medical Writing and Education [Internet]. 2025;4:162. Disponible en: http://dx.doi.org/10.56294/mw2025162

Auza-Santiváñez JC, Carías Díaz JA, Vedia Cruz OA, Robles-Nina SM, Escalante CS, Apaza Huanca B. Interactive formats: considerations for scientific publications. Seminars in Medical Writing and Education [Internet]. 2023;2:27. Disponible en: http://dx.doi.org/10.56294/mw202327

Auza Santiváñez JC, Carías Díaz JA, Vedia Cruz OA, Robles Nina SM, Sánchez Escalante C, Apaza Huanca B. mHealth in health systems: barriers to implementation. Health Leadership and Quality of Life [Internet]. 2022;1:7. Disponible en: http://dx.doi.org/10.56294/hl20227

Published

2025-02-09

How to Cite

1.
Carías A, Auza-Santivañez JC, Carías P, Díaz Bonilla V, Bautista-Vanegas FE, Vallejos-Rejas DRE, et al. Artificial intelligence in the Otorhinolaryngology class. Seminars in Medical Writing and Education [Internet]. 2025 Feb. 9 [cited 2025 Mar. 10];4:160. Available from: https://mw.ageditor.ar/index.php/mw/article/view/160