How Can Large Language Models Improve Health Research Skills: A Perspective Study.

BACKGROUND AND AIMS: Health researchers often struggle with challenges such as translating complex texts, interpreting nuanced information, conducting detailed statistical analyses, and more. Artificial Intelligence (AI) models, by providing easy access to information, offer a promising solution to these challenges. This study aims to examine university students' perspectives on the impact of AI models in overcoming these challenges. METHODS: This survey study was conducted in 2023 by using a researcher-developed questionnaire. A total of 445 students from the Kerman University of Medical Sciences participated and completed the questionnaire. Descriptive statistics and linear regression were used to analyze the relationship between independent variables and the impact of AI models on health research. Open-ended responses were coded and stored in MAXQDA. RESULTS: Sixty percent of students reported moderate or greater familiarity with AI models, while only 52% had moderate or greater usage of these tools. Over 70% considered the associated risks high. Participants rated AI models most favorably for "Translation of an article" (Mean = 3.33, SD = 1.07), "Article rephrasing" (Mean = 3.33, SD = 1.07), and "Reference search" (Mean = 3.27, SD = 1.14), with the lowest ratings for "Article submissions" (Mean = 3.10, SD = 1.01), "Data analysis" (Mean = 3.23, SD = 1.01), and "Proposal submissions" (Mean = 3.23, SD = 0.91), respectively. The most important applications of AI models were noted in "Translation of research" (Mean = 3.44), "Grammer checking" (Mean = 3.46, SD = 1.12), and "Text editing" (Mean = 3.35, SD = 1.14), while the least were in "Correspondence with journal editors" (Mean = 2.99, SD = 1.20), "Adherence to journal guidelines" (Mean = 3.07, SD = 1.17), and "Response to the reviewers' comments" (Mean = 3.12, SD = 1.13). "Providing false information" (n = 34), "Failure to apply legal and ethical standards in research" (n = 10), and a "Loss of ability and creativity among researchers" (n = 9) were the most significant risks of using AI models in health research. CONCLUSIONS: AI models show promise for improving research efficiency and quality, but further efforts are necessary to mitigate perceived risks and encourage broader adoption.