The Role of Artificial Intelligence in Radiology Residency Training: A National Survey Study

Emre Emekli; Özlem Coşkun; Işıl İrem Budakoğlu

doi:10.58600/eurjther2344

Authors

Emre Emekli Department of Radiology, Eskişehir Osmangazi University, Faculty of Medicine, Eskişehir, Türkiye, Department of Medical Education, Gazi University, Institute of HealthSciences, Ankara, Türkiye https://orcid.org/0000-0001-5989-1897
Özlem Coşkun Department of Medical Education and Informatics, Gazi University, Faculty of Medicine, Ankara, Türkiye https://orcid.org/0000-0001-8716-1584
Işıl İrem Budakoğlu Department of Medical Education and Informatics, Gazi University, Faculty of Medicine, Ankara, Türkiye https://orcid.org/0000-0003-1517-3169

DOI:

https://doi.org/10.58600/eurjther2344

Keywords:

Artificial Intelligence, Radiology, Medical Education

Abstract

Objective: Artificial Intelligence (AI) offers opportunities for radiologists to enhance workflow efficiency, perform faster and repeatable segmentation, and detect lesions more easily. The aim of this study is to investigate the current knowledge and general attitudes of radiology resident physicians towards AI. Additionally, it seeks to assess the current state of AI/ML/DL education in radiology residency, the awareness and use of available educational resources.

Methods: A cross-sectional study was conducted using an online survey from October 2023 to February 2024. The survey included demographic data, AI knowledge, attitudes towards AI, and the role of AI in medical education. Survey questions were developed based on literature and reviewed by experts in medical education and radiology.

Results: The study included 155 participants (38.7% female) with an average age of 28.81±4.77 years. About 80.6% were aware of AI terms, with a mean knowledge score of 3.02±1.39 on a 7-point Likert scale. Most participants (90.3%) had no programming knowledge. Only 22.6% used AI tools occasionally. The majority (73.4%) believed AI would change radiology's future, though only 10.3% felt radiologists' jobs were at risk. Regarding AI education, 84.5% reported no formal training, and awareness of online resources was low.

Conclusion: The study found that while awareness of AI among radiology residents is high, their knowledge and practical use of AI tools are limited. AI education is largely absent from residency programs, and awareness of online educational resources is low. These findings highlight the need for integrating AI training into radiology education and increasing awareness of available resources.

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References

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