Attitudes toward artificial intelligence in radiology with learner needs assessment within radiology residency programmes: a national multi-programme survey.

Autor:	Ooi SKG; Department of Nuclear Medicine and Molecular Imaging, Division of Radiological Sciences, Singapore General Hospital, Singapore., Makmur A; Department of Diagnostic Imaging, National University Hospital, Singapore., Soon AYQ; Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore., Fook-Chong S; Division of Medicine, Singapore General Hospital, Singapore., Liew C; Department of Diagnostic Radiology, Changi General Hospital, Singapore., Sia SY; Department of Diagnostic Imaging, National University Hospital, Singapore., Ting YH; Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore., Lim CY; Department of Diagnostic Radiology, Division of Radiological Sciences, Singapore General Hospital, Singapore.
Jazyk:	angličtina
Zdroj:	Singapore medical journal [Singapore Med J] 2021 Mar; Vol. 62 (3), pp. 126-134. Date of Electronic Publication: 2019 Nov 04.
DOI:	10.11622/smedj.2019141
Abstrakt:	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. (Copyright: © Singapore Medical Association.)
Databáze:	MEDLINE
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