Radiology is a unique medical specialty that focuses on image interpretation and report generation with limited patient contact. Resident read-out sessions with teaching are a quintessential part of reporting workflow practices in teaching institutions. However, most radiologist-educators do not have formal training in teaching and learning experiences vary. The five-step 'microskills' model ('one-minute preceptor' technique) developed by Neher is an easily adopted teaching model that complements the workflow of the typical read-out session, and can be utilised by radiologists of varied teaching experience and seniority. The steps are: (a) get a commitment; (b) probe for supporting evidence; (c) teach general rules; (d) reinforce what was done right; and (e) correct mistakes. Feedback is important to the model and accounts for two out of five microskills. The teaching model emphasises knowledge application and establishing relevance, which is useful in engaging the millennial resident. It is easily assimilated and applied by radiologist-educators.
Curriculum ; Education, Medical ; methods ; Humans ; Internship and Residency ; Learning ; Physicians ; Preceptorship ; Radiographic Image Interpretation, Computer-Assisted ; Radiography ; Radiology ; education ; trends ; Teaching

INTRODUCTION: We aimed to assess the attitudes and learner needs of radiology residents and faculty radiologists regarding artificial intelligence (AI) and machine learning (ML) in radiology. METHODS: A web-based questionnaire, designed using SurveyMonkey, was sent out to residents and faculty radiologists in all three radiology residency programmes in Singapore. The questionnaire comprised four sections and aimed to evaluate respondents' current experience, attempts at self-learning, perceptions of career prospects and expectations of an AI/ML curriculum in their residency programme. Respondents' anonymity was ensured. RESULTS: A total of 125 respondents (86 male, 39 female; 70 residents, 55 faculty radiologists) completed the questionnaire. The majority agreed that AI/ML will drastically change radiology practice (88.8%) and makes radiology more exciting (76.0%), and most would still choose to specialise in radiology if given a choice (80.0%). 64.8% viewed themselves as novices in their understanding of AI/ML, 76.0% planned to further advance their AI/ML knowledge and 67.2% were keen to get involved in an AI/ML research project. An overwhelming majority (84.8%) believed that AI/ML knowledge should be taught during residency, and most opined that this was as important as imaging physics and clinical skills/knowledge curricula (80.0% and 72.8%, respectively). More than half thought that their residency programme had not adequately implemented AI/ML teaching (59.2%). In subgroup analyses, male and tech-savvy respondents were more involved in AI/ML activities, leading to better technical understanding. CONCLUSION A growing optimism towards radiology undergoing technological transformation and AI/ML implementation has led to a strong demand for an AI/ML curriculum in residency education.