Subscribe to RSS
![](/products/assets/desktop/img/oa-logo.png)
DOI: 10.1055/s-0044-1788605
Exploring Radiology Postgraduate Students' Engagement with Large Language Models for Educational Purposes: A Study of Knowledge, Attitudes, and Practices
Funding None.![](https://www.thieme-connect.de/media/10.1055-s-00050590/EFirst/lookinside/thumbnails/10-1055-s-0044-1788605_2443574-1.jpg)
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.
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 educationPublication 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/)
Thieme Medical and Scientific Publishers Pvt. Ltd.
A-12, 2nd Floor, Sector 2, Noida-201301 UP, India
-
References
- 1 Tokuç B, Varol G. Medical education in the era of advancing technology. Balkan Med J 2023; 40 (06) 395-399
- 2 Abd-Alrazaq A, AlSaad R, Alhuwail D. et al. Large language models in medical education: opportunities, challenges, and future directions. JMIR Med Educ 2023; 9: e48291
- 3 Akinci D'Antonoli T, Stanzione A, Bluethgen C. et al. Large language models in radiology: fundamentals, applications, ethical considerations, risks, and future directions. Diagn Interv Radiol 2024; 30 (02) 80-90
- 4 Sarangi PK, Narayan RK, Mohakud S, Vats A, Sahani D, Mondal H. Assessing the capability of ChatGPT, Google Bard, and Microsoft Bing in solving radiology case vignettes. Indian J Radiol Imaging 2023; 34 (02) 276-282
- 5 Das D, Kumar N, Longjam LA. et al. Assessing the capability of ChatGPT in answering first- and second-order knowledge questions on microbiology as per competency-based medical education curriculum. Cureus 2023; 15 (03) e36034
- 6 Mondal H, Mondal S, Podder I. Using ChatGPT for writing articles for patients' education for dermatological diseases: a pilot study. Indian Dermatol Online J 2023; 14 (04) 482-486
- 7 Ahn S. The impending impacts of large language models on medical education. Korean J Med Educ 2023; 35 (01) 103-107
- 8 Safranek CW, Sidamon-Eristoff AE, Gilson A, Chartash D. The role of large language models in medical education: applications and implications. JMIR Med Educ 2023; 9: e50945
- 9 Daungsupawong H, Wiwanitkit V. ChatGPT and radiology in the future: comment. Indian J Radiol Imaging 2023; 34 (02) 371-372
- 10 Kapilamoorthy TR. Clinical radiology: past, present, and future-whither are we going?. Indian J Radiol Imaging 2024; 34 (02) 361-364
- 11 Sarangi PK, Lumbani A, Swarup MS. et al. Assessing ChatGPT's Proficiency in Simplifying Radiological Reports for Healthcare Professionals and Patients. Cureus 2023; 15 (12) e50881
- 12 Bhayana R, Krishna S, Bleakney RR. Performance of ChatGPT on a radiology board-style examination: insights into current strengths and limitations. Radiology 2023; 307 (05) e230582
- 13 Kottlors J, Bratke G, Rauen P. et al. Feasibility of differential diagnosis based on imaging patterns using a large language model. Radiology 2023; 308 (01) e231167
- 14 Sarangi PK, Irodi A, Panda S, Nayak DSK, Mondal H. Radiological differential diagnoses based on cardiovascular and thoracic imaging patterns: perspectives of four large language models. Indian J Radiol Imaging 2023; 34 (02) 269-275
- 15 Rao A, Kim J, Kamineni M. et al. Evaluating GPT as an adjunct for radiologic decision making: GPT-4 versus GPT-3.5 in a breast imaging pilot. J Am Coll Radiol 2023; 20 (10) 990-997
- 16 Rau A, Rau S, Zoeller D. et al. A context-based chatbot surpasses trained radiologists and generic ChatGPT in following the ACR Appropriateness Guidelines. Radiology 2023; 308 (01) e230970
- 17 Biri SK, Kumar S, Panigrahi M, Mondal S, Behera JK, Mondal H. Assessing the utilization of large language models in medical education: insights from undergraduate medical students. Cureus 2023; 15 (10) e47468
- 18 Tung AYZ, Dong LW. Malaysian medical students' attitudes and readiness toward AI (artificial intelligence): a cross-sectional study. J Med Educ Curric Dev 2023; 10: 23 821205231201164
- 19 Buabbas AJ, Miskin B, Alnaqi AA. et al. Investigating students' perceptions towards artificial intelligence in medical education. Healthcare (Basel) 2023; 11 (09) 1298
- 20 Alkhaaldi SMI, Kassab CH, Dimassi Z. et al. Medical student experiences and perceptions of ChatGPT and artificial intelligence: cross-sectional study. JMIR Med Educ 2023; 9: e51302
- 21 Al Mohammad B, Aldaradkeh A, Gharaibeh M, Reed W. Assessing radiologists' and radiographers' perceptions on artificial intelligence integration: opportunities and challenges. Br J Radiol 2024; 97 (1156): 763-769
- 22 Li Q, Qin Y. AI in medical education: medical student perception, curriculum recommendations and design suggestions. BMC Med Educ 2023; 23 (01) 852
- 23 Sarangi PK, Mondal H. Response generated by large language models depends on the structure of the prompt. Indian J Radiol Imaging 2024; 34 (03) 574-575
- 24 Vagha S, Mishra V, Joshi Y. Reviving medical education through teachers training programs: a literature review. J Educ Health Promot 2023; 12: 277