Volume 34, Issue 239 (11-2024)
J Mazandaran Univ Med Sci 2024, 34(239): 88-95 |
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Rahimi Esbo S, Ghaemi-Amiri M, Mostafazadeh-Bora M. Assessment of Medical Students' Acceptance, Knowledge, Attitudes, and Readiness toward Artificial Intelligence. J Mazandaran Univ Med Sci 2024; 34 (239) :88-95
URL: http://jmums.mazums.ac.ir/article-1-21008-en.html
URL: http://jmums.mazums.ac.ir/article-1-21008-en.html
Abstract: (159 Views)
Background and purpose: Artificial intelligence (AI) is transforming numerous fields, particularly healthcare. In Iran, where AI is an emerging discipline, there is a notable gap in knowledge and understanding in this area. This study aimed to explore medical students' acceptance, knowledge, attitudes, and readiness regarding medical artificial intelligence.
Materials and methods: This descriptive cross-sectional study was conducted on 117 medical students selected through convenience sampling. The study utilized a structured questionnaire comprising four sections: demographic characteristics, readiness (22 items rated on a five-point Likert scale), acceptance (28 items rated on a five-point Likert scale), knowledge (8 items rated on a three-point Likert scale), and attitude toward artificial intelligence (13 items rated on a five-point Likert scale). Data were analyzed using SPSS version 27, employing descriptive statistics, independent t-tests, Pearson correlation tests, and regression analysis. A significance level of P<0.05 was considered statistically significant.
Results: The findings indicated that the mean levels of readiness (50.66±84.13), knowledge (23.17±27.3), and acceptance (25.95±63.14) were moderate, while the mean attitude level (51.46±01.6) was good. A direct and statistically significant relationship was observed among readiness, knowledge, acceptance, and attitude toward artificial intelligence (P<0.05), except for the relationship between readiness and attitude, which was not statistically significant (P=0.516). Regression analysis showed that attending artificial intelligence training courses (Beta=22.5, P=0.013) and knowledge about artificial intelligence (Beta=0.41, P<0.001) were strong predictors of readiness for medical artificial intelligence. These relationships remained statistically significant in both simple and multivariate linear regression analyses. Additionally, artificial intelligence usage and acceptance were identified as independent predictors of readiness in simple linear regression.
Conclusion: Medical students at Babol University of Medical Sciences showed a positive attitude toward artificial intelligence, indicating its growing relevance in medical education. These findings suggest that education planners should focus on improving students’ knowledge, readiness, and acceptance of AI through well-structured courses and training programs. Such efforts could help better prepare students for the increasing role of AI in healthcare.
Materials and methods: This descriptive cross-sectional study was conducted on 117 medical students selected through convenience sampling. The study utilized a structured questionnaire comprising four sections: demographic characteristics, readiness (22 items rated on a five-point Likert scale), acceptance (28 items rated on a five-point Likert scale), knowledge (8 items rated on a three-point Likert scale), and attitude toward artificial intelligence (13 items rated on a five-point Likert scale). Data were analyzed using SPSS version 27, employing descriptive statistics, independent t-tests, Pearson correlation tests, and regression analysis. A significance level of P<0.05 was considered statistically significant.
Results: The findings indicated that the mean levels of readiness (50.66±84.13), knowledge (23.17±27.3), and acceptance (25.95±63.14) were moderate, while the mean attitude level (51.46±01.6) was good. A direct and statistically significant relationship was observed among readiness, knowledge, acceptance, and attitude toward artificial intelligence (P<0.05), except for the relationship between readiness and attitude, which was not statistically significant (P=0.516). Regression analysis showed that attending artificial intelligence training courses (Beta=22.5, P=0.013) and knowledge about artificial intelligence (Beta=0.41, P<0.001) were strong predictors of readiness for medical artificial intelligence. These relationships remained statistically significant in both simple and multivariate linear regression analyses. Additionally, artificial intelligence usage and acceptance were identified as independent predictors of readiness in simple linear regression.
Conclusion: Medical students at Babol University of Medical Sciences showed a positive attitude toward artificial intelligence, indicating its growing relevance in medical education. These findings suggest that education planners should focus on improving students’ knowledge, readiness, and acceptance of AI through well-structured courses and training programs. Such efforts could help better prepare students for the increasing role of AI in healthcare.
Type of Study: Brief Report |
Subject:
Artificial Intelligence
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