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.
Keywords: ChatGPT; artificial intelligence; on-call radiology; resident education.
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