Radiographers’ knowledge, attitudes and expectations of artificial intelligence in medical imaging

Coakley, Sinead; Young, Rena; Moore, Niamh; England, Andrew; O’Mahony, Alexander T.; O’Connor, Owen J.; Maher, Michael M.; McEntee, Mark F.

doi:10.1016/j.radi.2022.06.020

Cited by 22 publications

(22 citation statements)

References 17 publications

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“…Yet, the general attitude of radiology staff toward AI is positive. In a recent survey, European radiographers declared excitement about AI (83%), although only 8% had been taught on this matter in their qualification studies[ 105 ].…”

Section: Discussionmentioning

confidence: 99%

Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver

Berbís¹,

Godino²,

Val³

et al. 2023

World J Gastroenterol

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Artificial intelligence (AI) has experienced substantial progress over the last ten years in many fields of application, including healthcare. In hepatology and pancreatology, major attention to date has been paid to its application to the assisted or even automated interpretation of radiological images, where AI can generate accurate and reproducible imaging diagnosis, reducing the physicians’ workload. AI can provide automatic or semi-automatic segmentation and registration of the liver and pancreatic glands and lesions. Furthermore, using radiomics, AI can introduce new quantitative information which is not visible to the human eye to radiological reports. AI has been applied in the detection and characterization of focal lesions and diffuse diseases of the liver and pancreas, such as neoplasms, chronic hepatic disease, or acute or chronic pancreatitis, among others. These solutions have been applied to different imaging techniques commonly used to diagnose liver and pancreatic diseases, such as ultrasound, endoscopic ultrasonography, computerized tomography (CT), magnetic resonance imaging, and positron emission tomography/CT. However, AI is also applied in this context to many other relevant steps involved in a comprehensive clinical scenario to manage a gastroenterological patient. AI can also be applied to choose the most convenient test prescription, to improve image quality or accelerate its acquisition, and to predict patient prognosis and treatment response. In this review, we summarize the current evidence on the application of AI to hepatic and pancreatic radiology, not only in regard to the interpretation of images, but also to all the steps involved in the radiological workflow in a broader sense. Lastly, we discuss the challenges and future directions of the clinical application of AI methods.

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Section: Discussionmentioning

confidence: 99%

Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver

Berbís¹,

Godino²,

Val³

et al. 2023

World J Gastroenterol

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“…Technology Support e application practice of Internet advertising in programmatic marketing and big data marketing involves a lot of technical and theoretical knowledge of algorithms and machine learning. By collecting and sorting out relevant literature on the development of various Internet advertisements, the research object is preliminarily determined as computational advertising [26].…”

Section: Research Ideas Methods Andmentioning

confidence: 99%

Study on Optimization of Marketing Communication Strategies in the Era of Artificial Intelligence

Wen¹,

Lin

Guo

2022

Mobile Information Systems

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This paper starts from the research of computational advertising and scene theory, based on the background of artificial intelligence technology application, and studies the communication strategy of the computational advertising scene. It studies the three major strategies of computing advertising scene communication and deeply analyzes the principles and applications of scene insight strategy, content selection strategy, and community operation strategy. Results show the following: (1) under the background that the era of artificial intelligence has arrived, computing advertising uses artificial intelligence algorithms to complete breakthrough upgrades. Through the combination of algorithms and data, its intelligent upgrades are mainly manifested in three aspects: First, to achieve higher matching accuracy communication, the second is efficient customized communication, and the third is to realize the contextual interaction between advertisements and users; (2) the “intelligent” performance characteristics of intelligent scene dissemination of computing advertising are mainly in the application of scene intelligence technology, the intelligence of the data platform, and the intelligent construction of user portraits; (3) in the era of artificial intelligence, the communication strategies for computing advertising scenarios mainly include intelligent scenario insight strategies, content selection strategies, and community operation strategies. First, the intelligent scene insight strategy is mainly analyzed from two levels. On the one hand, it is a scene mining based on intelligent data and an in-depth analysis of the user tag system centered on intelligent algorithms. On the other hand, through the research on keyword search, the impact of intelligent upgrade on scenario insight is analyzed. Secondly, in terms of content selection strategy, the application of artificial intelligence technology has brought a new upgrade to the creation and recommendation mechanism of computational advertisements.

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“…Perceived familiarity with AI varied widely across studies, ranging from as low as 2.0% to as high as 95.8%. Additionally, four studies reported that most radiology specialists lacked education related to AI [42,49,52,54]. Most participants (25.6%-98.7%, median = 84.8%) expressed approval with respect to integrating AI-related content into the educational curricula of radiology residents [12,44,48 about AI, particularly its clinical applications, pros and cons, and technical methods [14,45,48,49,52,65,66].…”

Section: Effect Of Ai In Improving Accuracymentioning

confidence: 99%

“…Additionally, four studies reported that most radiology specialists lacked education related to AI [42,49,52,54]. Most participants (25.6%-98.7%, median = 84.8%) expressed approval with respect to integrating AI-related content into the educational curricula of radiology residents [12,44,48 about AI, particularly its clinical applications, pros and cons, and technical methods [14,45,48,49,52,65,66]. Three studies found that many radiologists believed human expertise will remain critical in developing and validating AI applications [45,57,66].…”

Section: Effect Of Ai In Improving Accuracymentioning

confidence: 99%

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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Radiographers’ knowledge, attitudes and expectations of artificial intelligence in medical imaging

Cited by 22 publications

References 17 publications

Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver

Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver

Study on Optimization of Marketing Communication Strategies in the Era of Artificial Intelligence

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

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