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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
› Institutsangaben Funding None.
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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



Publikationsverlauf

Artikel online veröffentlicht:
19. Juli 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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  • References

  • 1 Tokuç B, Varol G. Medical education in the era of advancing technology. Balkan Med J 2023; 40 (06) 395-399
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    PubMedSuche in Google Scholar
  • 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
    PubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 7 Ahn S. The impending impacts of large language models on medical education. Korean J Med Educ 2023; 35 (01) 103-107
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 9 Daungsupawong H, Wiwanitkit V. ChatGPT and radiology in the future: comment. Indian J Radiol Imaging 2023; 34 (02) 371-372
    PubMedSuche in Google Scholar
  • 10 Kapilamoorthy TR. Clinical radiology: past, present, and future-whither are we going?. Indian J Radiol Imaging 2024; 34 (02) 361-364
    Thieme ConnectPubMedSuche in Google Scholar
  • 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
    PubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    PubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    PubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 19 Buabbas AJ, Miskin B, Alnaqi AA. et al. Investigating students' perceptions towards artificial intelligence in medical education. Healthcare (Basel) 2023; 11 (09) 1298
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 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
    CrossrefPubMedSuche in Google Scholar
  • 22 Li Q, Qin Y. AI in medical education: medical student perception, curriculum recommendations and design suggestions. BMC Med Educ 2023; 23 (01) 852
    CrossrefPubMedSuche in Google Scholar
  • 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
    Thieme ConnectPubMedSuche in Google Scholar
  • 24 Vagha S, Mishra V, Joshi Y. Reviving medical education through teachers training programs: a literature review. J Educ Health Promot 2023; 12: 277
    CrossrefPubMedSuche in Google Scholar