An insight into the current perceptions of UK radiographers on the future impact of AI on the profession: A cross-sectional survey

Rainey, Clare; O’Regan, Tracy; Matthew, Jacqueline; Skelton, Emily; Woznitza, N.; Chu, Kwun-Ye; Goodman, Spencer; McConnell, Jonathan; Hughes, Ciara; Bond, Raymond; Malamateniou, Christina; McFadden, Sonyia

doi:10.1016/j.jmir.2022.05.010

Cited by 15 publications

(7 citation statements)

References 27 publications

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“…In the remaining articles, AI was used in detecting thyroid nodules [36], acute fractures [37], and intracranial hemorrhage [38] as well as checking endotracheal tube (ETT) placement [39]. Thirty‐six studies assessed radiologists' attitudes or views regarding the application of AI in radiology, including the opinions of radiologists ( n = 26) [12–14, 40–62], general health care professionals ( n = 1) [63], residents ( n = 4) [64–67], and physicians ( n = 1) [35]. Several studies in our review examined the viewpoints of multiple health professional groups.…”

Section: Resultsmentioning

confidence: 99%

“…Additionally, 43%–86% (median = 64.0%) of respondents in nine studies agreed that AI could increase clinical efficiency in radiology practice [12, 14, 43, 45, 51, 59, 61, 64, 66]. Three studies also reported that AI optimized workflow [40] and improved patient outcomes [12, 51].…”

Section: Resultsmentioning

confidence: 99%

“…Specifically, 29.5%-94.3% (median = 67.4%) of respondents in seven studies said they believed that AI has the potential to enhance clinical accuracy in radiology practice [12,14,40,41,46,63,66,67]. Additionally, 43%-86% (median = 64.0%) of respondents in nine studies agreed that AI could increase clinical efficiency in radiology practice [12,14,43,45,51,59,61,64,66]. Three studies also reported that AI optimized workflow [40] and improved patient outcomes [12,51].…”

Section: Effect Of Ai In Improving Accuracymentioning

confidence: 99%

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Efficiency, accuracy, and health professional's perspectives regarding artificial intelligence in radiology practice: A scoping review

He,

Liu,

et al. 2024

iRADIOLOGY

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In this scoping review, we evaluated the performance of artificial intelligence (AI) in clinical radiology practice and examined health professionals' perspectives regarding AI use in radiology. This review followed the Joanna Briggs Institute (JBI) methodological guidelines. We searched multiple databases and the gray literature from March 15, 2016 to December 31, 2023. Of 49 articles reviewed, 13 assessed the performance of AI in radiology clinical practice, and 36 examined the attitudes of health professionals toward the use of AI in radiology. In four separate studies, AI significantly improved the diagnostic sensitivity or detection rate. Furthermore, six articles emphasized a significant reduction in case reading times with AI use. Although three studies suggested an increase in specificity with the assistance of AI, these findings did not reach statistical significance. Health professionals expressed the belief that AI would have a significant impact on radiology but would not replace radiologists in the near future. Limited knowledge of AI was observed among health professionals, who supported increased education and explicit regulations and guidelines related to AI. Overall, AI can enhance diagnostic efficiency and accuracy in clinical radiology practice. However, knowledge gaps and the concerns of health professionals should be addressed by prioritizing education and reinforcing ethical and legal regulations to facilitate the advancement of AI use in radiology. This scoping review provides evidence toward a comprehensive understanding of AI's potential in clinical radiology practice, promoting its use and stimulating further discussion on related challenges and implications.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Effect Of Ai In Improving Accuracymentioning

confidence: 99%

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Efficiency, accuracy, and health professional's perspectives regarding artificial intelligence in radiology practice: A scoping review

He,

Liu,

et al. 2024

iRADIOLOGY

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“…With the increasing use of new technologies in the clinical setting a simple and reliable means to identify any important corelations in how the human is predicted to interact with the system, and indeed, how the interaction may result in a negative patient outcome, will be useful to target education and training interventions. This may mean that those who are engaged as clinical experts in the new systems, as suggested by Rainey et al (2022) and Strohm et al (2020) [32,33] can have a more detailed knowledge of the specific intervention and support needed, both before implementation and during clinical use.…”

Section: Plos Digital Healthmentioning

confidence: 99%

An experimental machine learning study investigating the decision-making process of students and qualified radiographers when interpreting radiographic images

Rainey,

Villikudathil,

McConnell

et al. 2023

PLOS Digit Health

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AI is becoming more prevalent in healthcare and is predicted to be further integrated into workflows to ease the pressure on an already stretched service. The National Health Service in the UK has prioritised AI and Digital health as part of its Long-Term Plan. Few studies have examined the human interaction with such systems in healthcare, despite reports of biases being present with the use of AI in other technologically advanced fields, such as finance and aviation. Understanding is needed of how certain user characteristics may impact how radiographers engage with AI systems in use in the clinical setting to mitigate against problems before they arise. The aim of this study is to determine correlations of skills, confidence in AI and perceived knowledge amongst student and qualified radiographers in the UK healthcare system. A machine learning based AI model was built to predict if the interpreter was either a student (n = 67) or a qualified radiographer (n = 39) in advance, using important variables from a feature selection technique named Boruta. A survey, which required the participant to interpret a series of plain radiographic examinations with and without AI assistance, was created on the Qualtrics survey platform and promoted via social media (Twitter/LinkedIn), therefore adopting convenience, snowball sampling This survey was open to all UK radiographers, including students and retired radiographers. Pearson’s correlation analysis revealed that males who were proficient in their profession were more likely than females to trust AI. Trust in AI was negatively correlated with age and with level of experience. A machine learning model was built, the best model predicted the image interpreter to be qualified radiographers with 0.93 area under curve and a prediction accuracy of 93%. Further testing in prospective validation cohorts using a larger sample size is required to determine the clinical utility of the proposed machine learning model.

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“…Additionally, it may be possible to gain additional information by employing quantitative measurements that may supply complementary information greater than that provided by noninvasive methods (14). A method of analyzing imaging data uses radiomics to automatically extract high-dimensional features.…”

Section: Introductionmentioning

confidence: 99%

An assessment of PET and CMR radiomic features for the detection of cardiac sarcoidosis

Mushari,

Soultanidis,

Duff

et al. 2024

Front. Nucl. Med.

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BackgroundVisual interpretation of PET and CMR may fail to identify cardiac sarcoidosis (CS) with high specificity. This study aimed to evaluate the role of [18F]FDG PET and late gadolinium enhancement (LGE)-CMR radiomic features in differentiating CS from another cause of myocardial inflammation, in this case patients with cardiac-related clinical symptoms following COVID-19.Methods[18F]FDG PET and LGE-CMR were treated separately in this work. There were thirty-five post-COVID-19 (PC) and forty CS datasets. Regions of interest were delineated manually around the entire left ventricle for PET and LGE-CMR datasets. Radiomic features were then extracted. The ability of individual features to correctly identify image data as CS or PC was tested to predict clinical classification of CS vs. PC using Mann–Whitney U-tests and logistic regression. Features were retained if P-value <0.00053, AUC >0.5 and accuracy >0.7. After applying correlation test, uncorrelated features were used as a signature (joint features) to train machine learning classifiers. For LGE-CMR analysis, to further improve the results, different classifiers were used for individual features besides logistic regression and the results of individual features of each classifier were screened to create a signature that include all features that followed the previously mentioned criteria and use them as input for machine learning classifiers.ResultsThe Mann–Whitney U-tests and logistic regression were trained on individual features to build a collection of features. For [18F]FDG PET analysis, the maximum target-to-background ratio (TBRmax) showed high area under the curve (AUC) and accuracy with small P-values (<0.00053) but the signature performed better (AUC 0.98 and accuracy 0.91). For LGE-CMR analysis, Gray Level Dependence Matrix (gldm)-Dependence Non-Uniformity showed good results with small error bars (accuracy 0.75 and AUC 0.87). However, by applying a Support Vector Machine classifier on individual LGE-CMR features and creating a signature, a Random Forest classifier displayed better AUC and accuracy (0.91 and 0.84, respectively).ConclusionUsing radiomic features may prove useful in identifying individuals with CS. Some features showed promising results to differentiate between PC and CS. By automating the analysis, the patient management process can be accelerated and improved.

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An insight into the current perceptions of UK radiographers on the future impact of AI on the profession: A cross-sectional survey

Cited by 15 publications

References 27 publications

Efficiency, accuracy, and health professional's perspectives regarding artificial intelligence in radiology practice: A scoping review

Efficiency, accuracy, and health professional's perspectives regarding artificial intelligence in radiology practice: A scoping review

An experimental machine learning study investigating the decision-making process of students and qualified radiographers when interpreting radiographic images

An assessment of PET and CMR radiomic features for the detection of cardiac sarcoidosis

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