Machine Learning in Otorhinolaryngology, Head and Neck Surgery and its applications in diagnosis and management: Undergraduates Perception toward New Era

Abstract

Background & aim: Machine learning (ML) is a growing field concerned with predicting novel situations from previous observations. The aim of this study was to determine medical students’ perceptions of ML in otorhinolaryngology, head, and neck surgery and its applications in diagnosis and management. Also, to assess medical students’ awareness of current challenges facing the application of ML in medical practice in the Kingdom of Saudi Arabia (KSA). Methods: A cross-sectional survey was conducted in February–May 2021 among medical students in Saudi Arabia. The participants were provided with questionnaires of the survey using electronic forms. Validation of the questionnaire was done using exploratory factor analysis and confirmatory factor analysis. There were 8 validated items on Attitude and 6 items on Knowledge. Results: A total of 538 students completed the questionnaire. The majority of the students were familiar with machine learning in general 308 (57.3%). However, only a few of the participants were familiar with machine learning applications in the field of otorhinolaryngology, head, and neck surgery 184 (34.2%). There was a significant difference between knowledge and attitude with the current year of study and GPA score, however, gender had no difference, yet there was a significant association between attitude among male and female medical students. Conclusion: Medical students in the KSA demonstrated a good knowledge of ML in general, although many were not familiar with machine learning applications in the field. Key words: Machine learning, Otorhinolaryngology, Medical student, Kingdom of Saudi Arabia

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