Artificial intelligence in radiology: trainees want more

O.-U. Hashmi; N. Chan; C.F. de Vries; A. Gangi; L. Jehanli; G. Lip

doi:10.1016/j.crad.2022.12.017

Artificial intelligence in radiology: trainees want more

O.-U. Hashmi^* (Corresponding Author), N. Chan, C.F. de Vries, A. Gangi, L. Jehanli, G. Lip

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

Aim To understand the attitudes of UK radiology trainees towards artificial intelligence (AI) in Radiology, in particular, assessing the demand for AI education. Materials and methods A survey, which ran over a period of 2 months, was created using the Google Forms platform and distributed via email to all UK training programmes. Results The survey was completed by 149 trainee radiologists with at least one response from all UK training programmes. Of the responses, 83.7% were interested in AI use in radiology but 71.4% had no experience of working with AI and 79.9% would like to be involved in AI-based projects. Almost all (98.7%) felt that AI should be taught during their training, yet only one respondent stated that their training programme had implemented AI teaching. Respondents indicated that basic understanding, implementation, and critical appraisal of AI software should be prioritized in teaching. Of the trainees, 74.2% agreed that AI would enhance the job of diagnostic radiologists over the next 20 years. The main concerns raised were information technology/implementation and ethical/regulatory issues. Conclusion Despite the current limited availability of AI-based activities and teaching within UK training programmes, UK trainees' attitudes towards AI are mostly positive with many showing interest in being involved with AI-based projects, activities, and teaching.

Original language	English
Pages (from-to)	e336-e341
Number of pages	6
Journal	Clinical Radiology
Volume	78
Issue number	4
Early online date	19 Jan 2023
DOIs	https://doi.org/10.1016/j.crad.2022.12.017
Publication status	Published - Apr 2023

Bibliographical note

Acknowledgements
The authors thank the survey respondents for their invaluable contribution. The authors also thank the members of RADIANT, Junior Radiologists' Forum (JRF), and the RCR for their aid in the dissemination of the survey. In addition, the authors are very grateful to Dr Stephen Harden, Professor Margaret Hall-Craggs, as well as the RCR Academic, Audit and Quality Improvement committees for their support and guidance.

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title = "Artificial intelligence in radiology: trainees want more",

abstract = "Aim To understand the attitudes of UK radiology trainees towards artificial intelligence (AI) in Radiology, in particular, assessing the demand for AI education. Materials and methods A survey, which ran over a period of 2 months, was created using the Google Forms platform and distributed via email to all UK training programmes. Results The survey was completed by 149 trainee radiologists with at least one response from all UK training programmes. Of the responses, 83.7% were interested in AI use in radiology but 71.4% had no experience of working with AI and 79.9% would like to be involved in AI-based projects. Almost all (98.7%) felt that AI should be taught during their training, yet only one respondent stated that their training programme had implemented AI teaching. Respondents indicated that basic understanding, implementation, and critical appraisal of AI software should be prioritized in teaching. Of the trainees, 74.2% agreed that AI would enhance the job of diagnostic radiologists over the next 20 years. The main concerns raised were information technology/implementation and ethical/regulatory issues. Conclusion Despite the current limited availability of AI-based activities and teaching within UK training programmes, UK trainees' attitudes towards AI are mostly positive with many showing interest in being involved with AI-based projects, activities, and teaching.",

author = "O.-U. Hashmi and N. Chan and {de Vries}, C.F. and A. Gangi and L. Jehanli and G. Lip",

note = "Acknowledgements The authors thank the survey respondents for their invaluable contribution. The authors also thank the members of RADIANT, Junior Radiologists' Forum (JRF), and the RCR for their aid in the dissemination of the survey. In addition, the authors are very grateful to Dr Stephen Harden, Professor Margaret Hall-Craggs, as well as the RCR Academic, Audit and Quality Improvement committees for their support and guidance.",

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AU - Hashmi, O.-U.

AU - Chan, N.

AU - de Vries, C.F.

AU - Gangi, A.

AU - Jehanli, L.

AU - Lip, G.

N1 - Acknowledgements The authors thank the survey respondents for their invaluable contribution. The authors also thank the members of RADIANT, Junior Radiologists' Forum (JRF), and the RCR for their aid in the dissemination of the survey. In addition, the authors are very grateful to Dr Stephen Harden, Professor Margaret Hall-Craggs, as well as the RCR Academic, Audit and Quality Improvement committees for their support and guidance.

PY - 2023/4

Y1 - 2023/4

N2 - Aim To understand the attitudes of UK radiology trainees towards artificial intelligence (AI) in Radiology, in particular, assessing the demand for AI education. Materials and methods A survey, which ran over a period of 2 months, was created using the Google Forms platform and distributed via email to all UK training programmes. Results The survey was completed by 149 trainee radiologists with at least one response from all UK training programmes. Of the responses, 83.7% were interested in AI use in radiology but 71.4% had no experience of working with AI and 79.9% would like to be involved in AI-based projects. Almost all (98.7%) felt that AI should be taught during their training, yet only one respondent stated that their training programme had implemented AI teaching. Respondents indicated that basic understanding, implementation, and critical appraisal of AI software should be prioritized in teaching. Of the trainees, 74.2% agreed that AI would enhance the job of diagnostic radiologists over the next 20 years. The main concerns raised were information technology/implementation and ethical/regulatory issues. Conclusion Despite the current limited availability of AI-based activities and teaching within UK training programmes, UK trainees' attitudes towards AI are mostly positive with many showing interest in being involved with AI-based projects, activities, and teaching.

AB - Aim To understand the attitudes of UK radiology trainees towards artificial intelligence (AI) in Radiology, in particular, assessing the demand for AI education. Materials and methods A survey, which ran over a period of 2 months, was created using the Google Forms platform and distributed via email to all UK training programmes. Results The survey was completed by 149 trainee radiologists with at least one response from all UK training programmes. Of the responses, 83.7% were interested in AI use in radiology but 71.4% had no experience of working with AI and 79.9% would like to be involved in AI-based projects. Almost all (98.7%) felt that AI should be taught during their training, yet only one respondent stated that their training programme had implemented AI teaching. Respondents indicated that basic understanding, implementation, and critical appraisal of AI software should be prioritized in teaching. Of the trainees, 74.2% agreed that AI would enhance the job of diagnostic radiologists over the next 20 years. The main concerns raised were information technology/implementation and ethical/regulatory issues. Conclusion Despite the current limited availability of AI-based activities and teaching within UK training programmes, UK trainees' attitudes towards AI are mostly positive with many showing interest in being involved with AI-based projects, activities, and teaching.

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VL - 78

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JO - Clinical Radiology

JF - Clinical Radiology

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Artificial intelligence in radiology: trainees want more

Abstract

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