How do medical students perceive the role of artificial intelligence in management of gastroesophageal reflux disease?

Gastroesophageal reflux treatment; Gastroesophageal reflux diagnosis; Cross-sectional method; Scale analysis (Psychology); Hydrogen-ion concentration; Qualitative research; Cronbach's alpha; Data analysis; Therapeutics; Health occupations students; Artificial intelligence; Questionnaires; Kruskal-Wallis Test; Descriptive statistics; Mann Whitney U Test; Medical students; Hospital medical staff; Statistics; Computer-aided diagnosis; Computers in medicine; Student attitudes; Data analysis software; Confidence intervals

Background: Artificial intelligence (AI) has significantly revolutionized the diagnosis and treatment of various medical and surgical conditions, including gastroesophageal reflux disease (GORD). AI has the potential to enhance diagnostic and treatment capabilities, contributing to overall advancements in healthcare. The current study aimed to investigate the medical students' views regarding the use of AI in GORD management. Methods: An anonymous, self-administered questionnaire was distributed among undergraduate medical students of various grades within different national medical institutions. The questionnaire comprised three sections, addressing sociodemographic data, knowledge, and attitudes. Knowledge and attitudes were assessed through 5- and 7-item questionnaires, respectively. The knowledge scores constituted a scale of 0–5. This reflected varying levels of understanding. Categories include poor knowledge (two or less), moderate knowledge (three), and good knowledge (4 and 5). Attitudes were classified as negative, neutral, or positive based on 50% and 75% cutoff points, with scores below 50% indicating negative attitudes, 50–75% considered neutral, and scores above 75% reflecting positive attitudes. Results: A total of 506 medical students participated, including 273 females and 233 males, with a ratio of 1.2–1. The majority fell within the age range of 20–26 years. Additionally, 388 participants (76.7%) reported grade point averages (GPAs) higher than 4. Regarding knowledge, 9% demonstrated a poor score of knowledge, while 33% had a moderate knowledge score. However, 65% of the participating students held a neutral attitude toward the role of AI in GORD management, with 6.9% expressing a negative stance on the matter. Conclusion: Although AI is highly involved in GORD management, the study revealed that medical students and interns possess a limited perception of this emerging technology. This may highlight the necessity for active involvement in AI education within the medical curricula. [ABSTRACT FROM AUTHOR]

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