Perspectives On Machine Learning and Artificial Intelligence from Trainee Radiologists

Congress:

ECR 2022

Poster Number:

C-21806

Type:

Scientific Exhibit

Keywords:

Computer applications, Management, Plain radiographic studies, Education, Technology assessment, Education and training, Workforce

Authors:

R. S. Clark, M. Porcheron, M. Jones, P. Wardle, V. E. Whitchurch

DOI:

10.26044/ecr2022/C-21806

DOI-Link:

https://dx.doi.org/10.26044/ecr2022/C-21806

Conclusion

We identified three key themes of perception on ML and AI; an awareness of the ubiquity of these systems in the medical field but a lack of distinct knowledge regarding the origin and nature of the data they use to “learn”, a general lack of trust in ML-based tools to operate as effectively as a human co-worker, and a cohesive group response to many prompts through discussion before, during and after formulation of an idea. 

Whilst there appears to be optimism regarding the presence of machine learning in radiology, those entering training still show inherent skeptism of these systems. This provides an avenue for future work to examine potential ways to address it. 

Fig 3: National Imaging Academy Meeting Room, National Imaging Academy, Wales 2021

Fig 1: Questionnaire results from the prompt "How Effective Is This Machine Learning...

Fig 2: Questionnaire results from the prompt "How Much Human Intervention Is Required...