Artificial intelligence in radiology: the ecosystem essential to improving patient care

Sogani, Julie; Allen, Bibb; Dreyer, Keith; McGinty, Geraldine

doi:10.1016/j.clinimag.2019.08.001

Cited by 26 publications

(22 citation statements)

References 26 publications

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“…We identified lacking acceptance as one of the most important causes for non-adoption, abandonment, and thus a barrier to the successful implementation of AI applications in radiology. The determinants of radiologists' acceptance of AI application found in this study are in line with evidence from surveys among radiologists [2,35] and radiology residents [31] and earlier evidence on the determinants of clinicians' acceptance of computerized decision support systems (CDSS): insufficient knowledge [5,6,36], trust [36,37], change in clinician's professional identity, and professional autonomy [36,38]. We found local champions to play a crucial role in overcoming lacking acceptance of technology users.…”

Section: Discussionsupporting

confidence: 89%

“…This element of job displacement due to automatization adds to the complexity of adoption and implementation processes in the field of health digitization. While the possibility of AI replacing radiologists, and thereby threatening their professional identity, was extensively mentioned in many recent radiology publications [5,9,16,44], none of the interviewees in this research identified with this threat. This aligns with recent opinion surveys conducted among radiologists [2,31,35].…”

Section: Discussionmentioning

confidence: 79%

“…Artificial intelligence (AI) is increasingly being recognized as an important application in clinical radiology [1][2][3][4][5]. Recent Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00330-020-06946-y) contains supplementary material, which is available to authorized users.…”

Section: Introductionmentioning

confidence: 99%

“…AI is expected to cause large changes in clinical work practices and requires complementary skills from radiologists [5,7,8]. A narrative of "radiologists becoming replaced by AI" has emerged, as discussions about this topic have flooded major conferences and scientific publications [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…Earlier forms of AI applications, such as the first computerized aided diagnosis (CAD) systems, have failed to achieve widespread adoption. Literature has mainly blamed this on low technical performance of these early applications [5,[14][15][16], while other potential barriers to successful implementation, such as organizational or social aspects, have been largely ignored [17].…”

Section: Introductionmentioning

confidence: 99%

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Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors

et al. 2020

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Objective The objective was to identify barriers and facilitators to the implementation of artificial intelligence (AI) applications in clinical radiology in The Netherlands. Materials and methods Using an embedded multiple case study, an exploratory, qualitative research design was followed. Data collection consisted of 24 semi-structured interviews from seven Dutch hospitals. The analysis of barriers and facilitators was guided by the recently published Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework for new medical technologies in healthcare organizations. Results Among the most important facilitating factors for implementation were the following: (i) pressure for cost containment in the Dutch healthcare system, (ii) high expectations of AI’s potential added value, (iii) presence of hospital-wide innovation strategies, and (iv) presence of a “local champion.” Among the most prominent hindering factors were the following: (i) inconsistent technical performance of AI applications, (ii) unstructured implementation processes, (iii) uncertain added value for clinical practice of AI applications, and (iv) large variance in acceptance and trust of direct (the radiologists) and indirect (the referring clinicians) adopters. Conclusion In order for AI applications to contribute to the improvement of the quality and efficiency of clinical radiology, implementation processes need to be carried out in a structured manner, thereby providing evidence on the clinical added value of AI applications. Key Points • Successful implementation of AI in radiology requires collaboration between radiologists and referring clinicians. • Implementation of AI in radiology is facilitated by the presence of a local champion. • Evidence on the clinical added value of AI in radiology is needed for successful implementation.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors

et al. 2020

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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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Color Shadows 2: Assessing the Impact of XAI on Diagnostic Decision-Making

Natali,

Famiglini,

Campagner

et al. 2023

Communications in Computer and Information Science

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Artificial intelligence in radiology: the ecosystem essential to improving patient care

Cited by 26 publications

References 26 publications

Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors

Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors

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

Color Shadows 2: Assessing the Impact of XAI on Diagnostic Decision-Making

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