To repeat or not to repeat: Radiologists demonstrated more decisiveness than their fellow radiographers in reducing the repeat rate during mobile chest radiography

Current and potential applications of artificial intelligence in medical imaging practice: A narrative review

BACKGROUND AND PURPOSE

Artificial intelligence (AI) is present in many areas of our lives. Much of the digital data generated in health care can be used for building automated systems to bring improvements to existing workflows and create a more personalised healthcare experience for patients. This review outlines select current and potential AI applications in medical imaging practice and provides a view of how diagnostic imaging suites will operate in the future. Challenges associated with potential applications will be discussed and healthcare staff considerations necessary to benefit from AI-enabled solutions will be outlined.

METHODS

Several electronic databases, including PubMed, ScienceDirect, Google Scholar, and University College Dublin Library Database, were used to identify relevant articles with a Boolean search strategy. Textbooks, government sources and vendor websites were also considered.

RESULTS/DISCUSSION

Many AI-enabled solutions in radiographic practice are available with more automation on the horizon. Traditional workflow will become faster, more effective, and more user friendly. AI can handle administrative or technical types of work, meaning it is applicable across all aspects of medical imaging practice.

CONCLUSION

AI offers significant potential to automate most of the manual tasks, ensure service consistency, and improve patient care. Radiographers, radiation therapists, and clinicians should ensure they have adequate understanding of the technology to enable ethical oversight of its implementation.

The assessment of image quality and diagnostic value in X-ray images: a survey on radiographers' reasons for rejecting images

BACKGROUND

Assessing the quality of diagnostic images is subjective and influenced by factors such education, skills, and experience of the assessor. This study aims to explore the radiographers' assessments of medical usefulness or rejection of X-ray images in specific cases.

RESULTS

Eighty-one radiographers from different countries responded to the questionnaire distributed online at the EFRS research HUB at ECR 2020 (a 15% response rate). Forty-two percent of the respondents practiced in the UK and Ireland. In addition to rejecting or keeping images in the presented 30 cases and giving a main reason for the images rejected, the participants explained their choice using comments, 1176 comments were obtained. Sixty percent of the comments were on kept images. The respondents kept on average 63% of the images. In the "Keep", "Could keep", and "Reject" categories on average 84%, 63% and 43% of images were kept respectively. The most common reasons given for rejecting an image were suboptimal positioning and centering. Potential diagnostic value and radiation protection were indicated as reasons to keep an image perceived as of low quality reported in n = 353 and n = 33 comments respectively.

CONCLUSIONS

There is an agreement internationally on what makes a good quality X-ray image. However, the opinion on medical usefulness of images of low or poor quality compared to image criteria varies. Diagnostic capability and radiation protection was the rationale used for keeping images not fulfilling image criteria. There seems to be a need for diagnostic quality to be included in image assessment in clinical practice.