CC BY-NC-ND 4.0 · Indian J Radiol Imaging 2023; 33(04): 440-449
DOI: 10.1055/s-0043-1772465
Original Article

Online for On Call: A Study Assessing the Use of Internet Resources Including ChatGPT among On-Call Radiology Residents in India

1   Department of Radiodiagnosis, Institute of Medical Sciences and SUM Hospital, Siksha ‘O’ Anusandhan deemed to be University, Odisha, India
Satya Mohapatra
1   Department of Radiodiagnosis, Institute of Medical Sciences and SUM Hospital, Siksha ‘O’ Anusandhan deemed to be University, Odisha, India
Chayasmita Mali
2   Department of Pathology, Kalinga Institute of Medical Sciences, Bhubaneswar, Odisha, India
Roopak Dubey
3   Department of Radiodiagnosis, Kalinga Institute of Medical Sciences, Bhubaneswar, Odisha, India
› Institutsangaben
Funding None.


Background The information-seeking behavior of the radiology residents on call has undergone modernization in the recent times given the advent of easy to access, reliable online resources, and robust artificial intelligence chatbots such as Chat Generative Pre-Trained Transformer (ChatGPT).

Purpose The aim of this study was to conduct a baseline analysis among the residents to understand the best way to meet information needs in the future, spread awareness about the existing resources, and narrow down to the most preferred online resource.

Methods and Materials A prospective, descriptive study was performed using an online survey instrument and was conducted among radiology residents in India. They were questioned on their demographics, frequency of on call, fatigue experienced on call, and preferred information resources and reasons for choosing them.

Results A total of 286 residents participated in the survey. All residents had used the Internet radiology resources during on-call duties. The most preferred resource material was Radiopaedia followed by Radiology Assistant. IMAIOS e-Anatomy was the most preferred anatomy resource. There was significant (p < 0.05) difference in relation to the use of closed edit peer-reviewed literature among the two batches with it being used almost exclusively by third year residents. In the artificial intelligence-aided ChatGPT section, 61.8% had used the software at least once while being on call, of them 57.6% responded that the information was inaccurate, 67.2% responded that the information was insufficient to aid in diagnosis, 100% felt that the lack of images in the software made it an unlikely resource that would be used by them in the future, and 85.8% agreed that they would use it for providing reporting templates in the future. In the suggestions for upcoming versions, 100% responded that images should be included in the description provide by the chatbot, and 74.5% felt that references for the information being provided should be included as it reaffirms the reliability of the information.

Conclusions Presently, we find that Radiopaedia met most of the requirements as an ideal online radiology resource according to the residents. In the present-day scenario, ChatGPT is not considered as an important on-call radiology education resource first because it lacks images which is quintessential for a budding radiologist, and second, it does not have any reference or proof for the information that it is providing. However, it may be of help to nonmedical professionals who need to understand radiology in layman's terms and to radiologists for patient report preparation and research writing.

Ethical Approval and Consent to Participate

Ethical approval and proper consent were taken from all residents participating in the study.

Authors Contributions

This study was directed and coordinated by HSS and SM; HSS, as the principal investigator, provided conceptual and technical guidance for all aspects of the project. SM and HSS planned and executed the study with the help from RD and CM. Analysis of the current resources was done by RD and CM. Literature search for available resources was suggested and executed by RD and HSS. Design of the study was done by CM and HSS. The manuscript was written by HSS and SM and commented on by all authors. All authors have read and approved the manuscript.


Artikel online veröffentlicht:
21. August 2023

© 2023. 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. (

Thieme Medical and Scientific Publishers Pvt. Ltd.
A-12, 2nd Floor, Sector 2, Noida-201301 UP, India

  • References

  • 1 Zafar S, Safdar S, Zafar AN. Evaluation of use of e-Learning in undergraduate radiology education: a review. Eur J Radiol 2014; 83 (12) 2277-2287
  • 2 Kok EM, van Geel K, van Merriënboer JJ, Robben SG. What we do and do not know about teaching medical image interpretation. Front Psychol 2017; 8: 309
  • 3 Derakhshani A, Ding J, Vijayasarathi A. On-call radiology 2020: where trainees look for help in a high stakes and time sensitive environment. Clin Imaging 2021; 77: 219-223
  • 4 Kitchin DR, Applegate KE. Learning radiology a survey investigating radiology resident use of textbooks, journals, and the internet. Acad Radiol 2007; 14 (09) 1113-1120
  • 5 D'Alessandro MP, Nguyen BC, D'Alessandro DM. Information needs and information-seeking behaviors of on-call radiology residents. Acad Radiol 1999; 6 (01) 16-21 (Jan)
  • 6 Galliard F. Radiopaedia. Accessed May 20, 2023 at:
  • 7 El-Ali A, Kamal F, Cabral CL, Squires JH. Comparison of traditional and web based medical student teaching by radiology residents. J Am Coll Radiol 2019; 16 (4 Pt A): 492-495
  • 8 Elkassem AA, Smith AD. Potential uses cases for ChatGPT in radiology reporting. Am J Roentgenol 2023; 221 (03) 373-376