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. 2022 Nov-Dec;64(6):516-524.
doi: 10.1016/j.rxeng.2021.03.008.

Medical students' perceptions of the impact of artificial intelligence in radiology

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Free article

Medical students' perceptions of the impact of artificial intelligence in radiology

G Caparrós Galán et al. Radiologia (Engl Ed). 2022 Nov-Dec.
Free article

Abstract

Objectives: To analyse medical students' perceptions of the impact of artificial intelligence in radiology.

Material and methods: A structured questionnaire comprising 28 items organised into six sections was distributed to students of medicine in Spain in December 2019.

Results: A total of 341 students responded. Of these, 27 (7.9%) included radiology among their three main choices for specialization, and 51.9% considered that they clearly understood what artificial intelligence is. The overall rate of correct answers to the objective true-or-false questions about artificial intelligence was 70.7%. Whereas 75.9% expressed their disagreement with the hypothesis that artificial intelligence would replace radiologists, only 41.9% disagreed with the hypothesis that the demand for radiologists would decrease in the future. Only 36.7% expressed concerns about the role of artificial intelligence related to choosing radiology as a specialty. A greater proportion of students in the early years of medical school agreed with statements that radiologists accept artificial-intelligence-related technological changes and work with the industry to apply them as well as with statements about the need to include basic training about artificial intelligence in the medical school curriculum.

Conclusions: The students surveyed are aware of the impact of artificial intelligence in daily life, but not of the current debate about its potential applications in radiology. In general, they think that artificial intelligence will revolutionise radiology without having an alarming effect on the employability of radiologists. The students surveyed think that it is necessary to provide basic training about artificial intelligence in undergraduate medical school programs.

Keywords: Artificial intelligence; Encuesta; Especialidad; Estudiantes de medicina; Inteligencia artificial; Medical students; Radiology; Radiología; Specialty; Survey.

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