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Vol. 64. Issue 6.
Pages 516-524 (November - December 2022)
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Vol. 64. Issue 6.
Pages 516-524 (November - December 2022)
Original articles
Medical students’ perceptions of the impact of artificial intelligence in radiology
Percepciones de estudiantes de Medicina sobre el impacto de la inteligencia artificial en radiología
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960
G. Caparrós Galán, F. Sendra Portero
Corresponding author
sendra@uma.es

Corresponding author.
Departamento de Radiología y Medicina Física, Facultad de Medicina, Málaga, Spain
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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
Radiology
Specialty
Medical students
Survey
Resumen
Objetivos

Analizar la percepción de alumnos de Medicina sobre el impacto de la inteligencia artificial (IA) en radiología.

Material y métodos

Se distribuyó una encuesta estructurada en 28 ítems, organizados en seis secciones, entre estudiantes de Medicina españoles durante diciembre de 2019.

Resultados

Respondieron 341 estudiantes, de los que 27 (7,9%) incluyeron la radiología entre sus tres opciones principales para elegir especialidad; el 51,9% consideró que entendía bien qué es la inteligencia artificial. La tasa de acierto global en preguntas objetivas verdadero/falso sobre inteligencia artificial fue del 70,7%, y un 75,9% expresó su desacuerdo con la hipótesis de un reemplazo futuro del radiólogo, mientras que el desacuerdo con una hipotética reducción de la demanda de radiólogos fue menor (41,9%). Solamente el 36,7% mostró preocupación por la inteligencia artificial a la hora de elegir radiología como especialidad. Los estudiantes de cursos inferiores se mostraron más de acuerdo con que los radiólogos acepten los cambios tecnológicos de la inteligencia artificial y trabajen con la industria para su aplicación y con la necesidad de incluir formación básica sobre inteligencia artificial en el currículo de medicina.

Conclusiones

Los estudiantes encuestados son conscientes del impacto de la inteligencia artificial en la vida diaria, pero desconocen el debate actual sobre sus potenciales aplicaciones en radiología. En general, piensan que la inteligencia artificial revolucionará la radiología, pero sin un impacto alarmante en la empleabilidad de los radiólogos. Los alumnos encuestados opinan que es necesario proporcionar formación básica sobre inteligencia artificial en pregrado.

Palabras clave:
Inteligencia artificial
Radiología
Especialidad
Estudiantes de medicina
Encuesta

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