Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey
By C. Sit, R. Srinivasan, A. Amlani, K. Muthuswamy, A. Azam, Leo Monzon, D. Poon. Insights into Imaging, 2020
391 citations recorded by the source corpus.
Objectives To explore the attitudes of United Kingdom (UK) medical students regarding artificial intelligence (AI), their understanding, and career intention towards radiology. We also examine the state of education relating to AI amongst this cohort. Methods UK medical students were invited to complete an anonymous electronic survey consisting of Likert and dichotomous questions. Results Four hundred eighty-four responses were received from 19 UK medical schools. Eighty-eight percent of students believed that AI will play an important role in healthcare, and 49% reported they were less likely to consider a career in radiology due to AI. Eighty-nine percent of students believed that teaching in AI would be beneficial for their careers, and 78% agreed that students should receive training in AI as part of their medical degree. Only 45 students received any teaching on AI; none of the students received such teaching as part of their compulsory curriculum. Statistically, students that did receive teaching in AI were more likely to consider radiology ( p = 0.01) and rated more positively to the questions relating to the perceived competence in the post-graduation use of AI ( p = 0.01–0.04); despite this, a large proportion of students in the taught group reported a lack of confidence and understanding required for the critical use of healthcare AI tools. Conclusions UK medical students understand the importance of AI and are keen to engage. Medical school training on AI should be expanded and improved. Realistic use cases and limitations of AI must be presented to students so they will not feel discouraged from pursuing radiology.
Subfields: Health Informatics, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/ff6914cef1a774d8fa4a1d84639ccf983265136d
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Citations in the corpus
- The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory Validation and Associations with Personality, Corporate Distrust, and General Trust (2022, background): “Recent work suggests that the impact of specific AI training is of relevance in AI attitudes (e.g., Ehsan et al., 2021; Sit et al., 2020).”
- Students’ voices on generative AI: perceptions, benefits, and challenges in higher education (2023, background): “Most students agreed that AI have a profound impact on their disciplines and future careers (e.g., Bisdas et al., 2021; Gong et al., 2019; Sit et al., 2020) and expressed an intention to utilise AI in their learning and future practice (e.g., Bisdas et al., 2021; Lee et al., 2022), and thus…”