Challenges of Radiology education in the era of artificial intelligence

Gorospe‐Sarasúa, Luis; Muñoz-Olmedo, J.M.; Portero, F. Sendra; Luis‐García, Rodrigo de

doi:10.1016/j.rxeng.2020.10.012

Cited by 16 publications

(10 citation statements)

References 28 publications

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“…Likewise, the type of school (public versus subsidized-private) seems to affect student outcomes, and so environmental variables influence the neural network, as in biological models (Berman et al, 2019). Regarding h5, it is confirmed that it is possible to design a model through an ANN capable of predicting the results of a series of variables from the field of educational psychology in the school population, following previous studies carried out with the educational community (Feng & Law, 2021;Martínez-Ramón et al, 2023), suggesting that there is a body of knowledge and evidence to support the feasibility of building this type of model, beyond other fields where its efficacy has also been demonstrated (Badman et al, 2020;Farivar et al, 2020;Gorospe-Sarasúa et al, 2021;Sánchez et al, 2021).…”

Section: Discussionmentioning

confidence: 55%

“…Within computational modelling and artificial intelligence (AI), artificial neural networks (ANNs) have been used in very diverse fields such as economics, engineering or medicine (Farivar et al, 2020;Gorospe-Sarasúa et al, 2021). However, the same has not been true for the educational field, although it is currently under development (Haghighi et al, 2021;Martínez-Ramón et al, 2023).…”

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confidence: 99%

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Do artificial neural networks dream of understanding sentence comprehension? A preliminary study ( ¿Sueñan las redes neuronales artificiales con entender la comprensión de frases? Un estudio preliminar )

Martínez-Ramón,

Gil

2023

Studies in Psychology

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Artificial neural networks (ANNs) are an emerging field with a positive and encouraging outlook. In education, it is postulated that attention and academic performance could explain reading outcomes. The main goal of this research was to study the predictive capacity of an ANN with a backpropagation algorithm by analysing the relationship between sentence and text reading comprehension efficiency, attentional variables and academic performance in third-grade primary school students (N = 183). A nonexperimental approach was adopted, using a cross-sectional and ex post facto design. Ten schools (70% public) located in southeastern Spain participated. Test of Reading Efficacy (TECLE), d2 attention test and TALE-2000 were administered. The results revealed that it is possible to design a network capable of learning by itself to predict sentence comprehension. Students who were good readers obtained better grades, concentrated better, scanned the stimulus more attentively, obtained more correct answers and made fewer omissions. The conclusions concerned the ethical implications of AI and the need to introduce ANNs in initial teacher training. RESUMENLas redes neuronales artificiales (RNA) son un campo emergente con perspectivas positivas y alentadoras. En educación, se postula que la atención y el rendimiento académico podrían explicar los resultados de la lectura. El objetivo principal de esta investigación fue estudiar la capacidad predictiva de una RNA con un algoritmo de retropropagación a través del análisis de la relación entre la eficacia en la comprensión lectora de frases y textos, las variables atencionales y el rendimiento académico en alumnos de 3° de primaria (N = 183). Se adoptó un enfoque no experimental, utilizando un diseño transversal y ex post facto. Participaron 10 colegios (70% públicos) situados en el sureste de España, a los que se administró el Test de Eficacia Lectora (TECLE), el test de atención d2 y el TALE-2000. Los resultados revelaron que es posible diseñar una red capaz de aprender por

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Section: Discussionmentioning

confidence: 55%

mentioning

confidence: 99%

Do artificial neural networks dream of understanding sentence comprehension? A preliminary study ( ¿Sueñan las redes neuronales artificiales con entender la comprensión de frases? Un estudio preliminar )

Martínez-Ramón,

Gil

2023

Studies in Psychology

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“…Few studies have been carried out in recent years addressing the need for AI education for both students and residents, as well as medical specialists. There seems to be a generalized and growing consensus to continue training radiologists who incorporate new knowledge and skills in said technologies, and that this training should begin in the university phase, be consolidated in residency and continue as part of continuous training throughout the profession [ 19 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Impact of the Rise of Artificial Intelligence in Radiology: What Do Students Think?

Barreiro-Ares

Morales-Santiago

Portero

et al. 2023

IJERPH

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The rise of artificial intelligence (AI) in medicine, and particularly in radiology, is becoming increasingly prominent. Its impact will transform the way the specialty is practiced and the current and future education model. The aim of this study is to analyze the perception that undergraduate medical students have about the current situation of AI in medicine, especially in radiology. A survey with 17 items was distributed to medical students between 3 January to 31 March 2022. Two hundred and eighty-one students correctly responded the questionnaire; 79.3% of them claimed that they knew what AI is. However, their objective knowledge about AI was low but acceptable. Only 24.9% would choose radiology as a specialty, and only 40% of them as one of their first three options. The applications of this technology were valued positively by most students, who give it an important Support Role, without fear that the radiologist will be replaced by AI (79.7%). The majority (95.7%) agreed with the need to implement well-established ethical principles in AI, and 80% valued academic training in AI positively. Surveyed medical students have a basic understanding of AI and perceive it as a useful tool that will transform radiology.

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“…The use of DL in NM, referring to the physics part, includes disease diagnosis using PET [19], SPECT [20,21], imaging physics using PET [22], SPECT [23], image reconstruction using PET [24], SPECT [25], image denoising using PET [26,27], SPECT [28], image segmentation using PET [29], SPECT [30], and image classification using PET [31], SPECT [32]. Similar ideas are presented in [2,[33][34][35], where examples of specific ML capabilities include automated image segmentation, pre-analysis, and quantitation, radiomic feature extraction from image data, image reconstruction, case triage and reporting prioritization, research and data mining, and natural language processing. The second component, primarily application-driven, will be referred to as the 'clinical' component.…”

Section: Introductionmentioning

confidence: 99%

Review of artificial intelligence clinical applications in Nuclear Medicine

Panagiotidis,

Papachristou,

Makridou

et al. 2023

Nuclear Medicine Communications

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This paper provides an in-depth analysis of the clinical applications of artificial intelligence (AI) in Nuclear Medicine, focusing on three key areas: neurology, cardiology, and oncology. Beginning with neurology, specifically Alzheimer’s disease and Parkinson’s disease, the paper examines reviews on diagnosis and treatment planning. The same pattern is followed in cardiology studies. In the final section on oncology, the paper explores the various AI applications in multiple cancer types, including lung, head and neck, lymphoma, and pancreatic cancer.

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Challenges of Radiology education in the era of artificial intelligence

Cited by 16 publications

References 28 publications

Do artificial neural networks dream of understanding sentence comprehension? A preliminary study ( ¿Sueñan las redes neuronales artificiales con entender la comprensión de frases? Un estudio preliminar )

Do artificial neural networks dream of understanding sentence comprehension? A preliminary study ( ¿Sueñan las redes neuronales artificiales con entender la comprensión de frases? Un estudio preliminar )

Impact of the Rise of Artificial Intelligence in Radiology: What Do Students Think?

Review of artificial intelligence clinical applications in Nuclear Medicine

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