Artificial intelligence in radiology: the ecosystem essential to improving patient care
Abstract
The rapid development of artificial intelligence (AI) has led to its widespread use in multiple industries, including healthcare. AI has the potential to be a transformative technology that will significantly impact patient care. Particularly, AI has a promising role in radiology, in which computers are indispensable and new technological advances are often sought out and adopted early in clinical practice. We present an overview of the basic definitions of common terms, the development of an AI ecosystem in imaging and its value in mitigating the challenges of implementation in clinical practice.
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While the possibility of AI replacing radiologists was extensively mentioned in recent radiology publications, none of the interviewees in this research identified with this threat.
“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.”
From Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors (2020) · cites this paper as [5]
AI is expected to cause large changes in clinical work practices and requires complementary skills from radiologists.
“AI is expected to cause large changes in clinical work practices and requires complementary skills from radiologists [5, 7, 8].”
From Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors (2020) · cites this paper as [5]
Literature has mainly blamed the failure of early CAD systems on low technical performance, while other potential barriers such as organizational or social aspects have been largely ignored.
“Literature has mainly blamed this on low technical performance of these early applications [5, 14–16], while other potential barriers to successful implementation, such as organizational or social aspects, have been largely ignored [17].”
From Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors (2020) · cites this paper as [5]
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- Authors
- Sogani, J., Allen, B., Dreyer, K., & McGinty, G.
- Affiliations
- Weill Cornell Medicine (US)
- Grandview Medical Center (US)
- Massachusetts General Hospital (US)
- Corresponding author
- Sogani, Julie
- McGinty, Geraldine
- Venue
- Clinical Imaging
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 59
- Issue
- 1
- Pages
- A3-A6
- ISSN
- 0899-7071 (ISSN-L)
- OpenAlex
- W2971129090
- Cited by
- 36
- References
- 29
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 6 · 2023: 6 · 2022: 4 · 2021: 4 · 2020: 10
- FWCI
- 1.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Clinical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Keywords
- artificial intelligence, radiology, AI ecosystem, clinical implementation, patient care, diagnostic imaging, AI terminology
- MeSH terms
- Artificial Intelligence, Diagnostic Imaging, Patient Care, Humans, Radiography, Radiology, Ecosystem
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