A Survey-Based Cross-Sectional Study of Undergraduate Medical Students' Expectations for Medical Education and Attitudes Regarding Artificial Intelligence in Medicine

Background: Medical technologies have been revolutionized by artificial intelligence (AI), which is widely recognized as a field of computer science that can solve challenging issues. Assessing medical students' acceptance and potential use of AIEd (Artificial Intelligence in Education) is essential in understanding the advantages and challenges of AI in medical education and promoting its successful integration. Understanding medical students' attitudes toward AI is also crucial in influencing their behaviour in the future. This cross-sectional study was carried out among undergraduate medical students in light of the described data in order to evaluate their knowledge, confidence, and perceived dependability of AI as well as their preferences for training related to AI. Materials and Methods: The study was conducted in a Tertiary Care Hospital at Puducherry. Convenience sampling was used to choose undergraduate medical students who met the study's qualifying requirements. A validated and pre-tested semi-structured questionnaire was used to gather data for the study. It has five sections: socio-demographic information, knowledge of artificial intelligence in medicine, attitudes toward AI in medicine, expectations for AI in medical education, and open-ended questions about opinions on AI in medical education. The data collected were entered in Microsoft Excel 2019 and the results were analyzed using SPSS software version 23.0 Results: The study found that most undergraduate medical students have neutral to generally positive opinions regarding AI and acknowledge its potential for help in diagnosis and therapy. The perceptions of the majority of participants may have been influenced by their lack of prior exposure to AI-related courses or real-world applications. Conclusion: In order to prepare aspiring doctors for the quickly changing technological landscape in healthcare, the results highlight the significance of including organized AI education in the medical curriculum.

Authors

Publication Details

Journal
International Journal of Environmental Sciences
Published
2025-09-01
DOI
https://doi.org/10.64252/pr9asp78
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Survey-Based Cross-Sectional Study of Undergraduate Medical Students' Expectations for Medical Education and Attitudes Regarding Artificial Intelligence in Medicine

M.K. Swamy, Suresh Kandagal Veerabhadrappa, K Nandhini, Prabhu Gnanasekaran
International Journal of Environmental Sciences
COVID-19 diagnosis using AI
article

A Survey-Based Cross-Sectional Study of Undergraduate Medical Students' Expectations for Medical Education and Attitudes Regarding Artificial Intelligence in Medicine

M.K. Swamy, Suresh Kandagal Veerabhadrappa, K Nandhini, Prabhu Gnanasekaran
article en

Abstract

Background: Medical technologies have been revolutionized by artificial intelligence (AI), which is widely recognized as a field of computer science that can solve challenging issues. Assessing medical students' acceptance and potential use of AIEd (Artificial Intelligence in Education) is essential in understanding the advantages and challenges of AI in medical education and promoting its successful integration. Understanding medical students' attitudes toward AI is also crucial in influencing their behaviour in the future. This cross-sectional study was carried out among undergraduate medical students in light of the described data in order to evaluate their knowledge, confidence, and perceived dependability of AI as well as their preferences for training related to AI. Materials and Methods: The study was conducted in a Tertiary Care Hospital at Puducherry. Convenience sampling was used to choose undergraduate medical students who met the study's qualifying requirements. A validated and pre-tested semi-structured questionnaire was used to gather data for the study. It has five sections: socio-demographic information, knowledge of artificial intelligence in medicine, attitudes toward AI in medicine, expectations for AI in medical education, and open-ended questions about opinions on AI in medical education. The data collected were entered in Microsoft Excel 2019 and the results were analyzed using SPSS software version 23.0 Results: The study found that most undergraduate medical students have neutral to generally positive opinions regarding AI and acknowledge its potential for help in diagnosis and therapy. The perceptions of the majority of participants may have been influenced by their lack of prior exposure to AI-related courses or real-world applications. Conclusion: In order to prepare aspiring doctors for the quickly changing technological landscape in healthcare, the results highlight the significance of including organized AI education in the medical curriculum.

International Journal of Environmental Sciences
Openalex Percentile: Top 66%
COVID-19 diagnosis using AI
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

A Survey-Based Cross-Sectional Study of Undergraduate Medical Students' Expectations for Medical Education and Attitudes Regarding Artificial Intelligence in Medicine — M.K. Swamy, Suresh Kandagal Veerabhadrappa, et al. · International Journal of Environmental Sciences (2025) | TGRS Research Map | TGRS