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CC BY-NC-ND 4.0 · Indian J Radiol Imaging 2025; 35(01): 035-042
DOI: 10.1055/s-0044-1788605
Original Article

Exploring Radiology Postgraduate Students' Engagement with Large Language Models for Educational Purposes: A Study of Knowledge, Attitudes, and Practices

Pradosh Kumar Sarangi 
1   Department of Radiodiagnosis, All India Institute of Medical Sciences, Deoghar, Jharkhand, India
,
Braja Behari Panda
2   Department of Radiodiagnosis, Veer Surendra Sai Institute of Medical Sciences and Research, Burla, Odisha, India
,
Sanjay P. 
3   Department of Radiodiagnosis, Mysore Medical College and Research Institute, Mysore, India
,
Debabrata Pattanayak 
2   Department of Radiodiagnosis, Veer Surendra Sai Institute of Medical Sciences and Research, Burla, Odisha, India
,
Swaha Panda 
4   Department of Otorhinolaryngology and Head and Neck Surgery, All India Institute of Medical Sciences, Deoghar, Jharkhand, India
,
Himel Mondal 
5   Department of Physiology, All India Institute of Medical Sciences, Deoghar, Jharkhand, India
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Abstract

Background The integration of large language models (LLMs) into medical education has received increasing attention as a potential tool to enhance learning experiences. However, there remains a need to explore radiology postgraduate students' engagement with LLMs and their perceptions of their utility in medical education. Hence, we conducted this study to investigate radiology postgraduate students' knowledge, attitudes, and practices regarding LLMs in medical education.

Materials and Methods A cross-sectional quantitative survey was conducted online on Google Forms. Participants from all over India were recruited via social media platforms and snowball sampling techniques. A previously validated questionnaire was used to assess knowledge, attitude, and practices regarding LLMs. Descriptive statistical analysis was employed to summarize participants' responses.

Results A total of 252 (139 [55.16%] males and 113 [44.84%] females) radiology postgraduate students with a mean age of 28.33 ± 3.32 years participated in the study. The majority of the participants (47.62%) were familiar with LLMs with their potential incorporation with traditional teaching–learning tools (71.82%). They are open to including LLMs as a learning tool (71.03%) and think that it would provide comprehensive medical information (62.7%). Residents take the help of LLMs when they do not get the desired information from books (46.43%) or Internet search engines (59.13%). The overall score of knowledge (3.52 ± 0.58), attitude (3.75 ± 0.51), and practice (3.15 ± 0.57) were statistically significantly different (analysis of variance [ANOVA], p < 0.0001), with the highest score in attitude and lowest in practice. However, no significant differences were found in the scores for knowledge (p = 0.64), attitude (p = 0.99), and practice (p = 0.25) depending on the year of training.

Conclusion Radiology postgraduate students are familiar with LLM and recognize the potential benefits of LLMs in postgraduate radiology education. Although they have a positive attitude toward the use of LLMs, they are concerned about its limitations and use it only in limited situations for educational purposes.

Keywords

medical students - radiology - artificial intelligence - large language model - ChatGPT - medical education

Supplementary Material



Publication History

Article published online:
19 July 2024

© 2024. Indian Radiological Association. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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