Artificial Intelligence Readiness Status of Medical Faculty Students

Abstract

Objective: This research aims to examine the knowledge level and awareness of Faculty of Medicine students about medical artificial intelligence technologies. Methods: In this study involving students studying at Medical Faculties in Turkey, descriptive questionnaire, and the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) were used. The suitability of continuous variables for normal distribution was tested with the Shapiro-Wilk test. Descriptive statistics for continuous variables are presented as mean and standard deviation or median (Q1-Q3). Descriptive statistics for categorical variables are reported as frequencies and percentages. Homogeneity of variances was evaluated with the Levene test. Mann Whitney U test was used to compare the scale subdimension and total scores according to two independent groups; One-way Analysis of Variance or Kruskal Wallis test was used to compare the scale subdimensions and total scores according to more than two independent groups. Dunn-Bonferroni test was used for multiple comparisons if there was a significant difference between the groups. The relationship between MAIRS-MS subdimensions and MAIRS-MS score was evaluated with the Spearman correlation coefficient. MAIRS-MS reliability was determined by Cronbach alpha value. The value of p

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