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Continuous Learning AI in Radiology: Implementation Principles and Early Applications
Abstract
Artificial intelligence (AI) is becoming increasingly present in radiology and health care. This expansion is driven by the principal AI strengths: automation, accuracy, and objectivity. However, as radiology AI matures to become fully integrated into the daily radiology routine, it needs to go beyond replicating static models, toward discovering new knowledge from the data and environments around it. Continuous learning AI presents the next substantial step in this direction and brings a new set of opportunities and challenges. Herein, the authors discuss the main concepts and requirements for implementing continuous AI in radiology and illustrate them with examples from emerging applications.
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From Blockchain and Artificial Intelligence Technology in E-Health (2021) · cites this paper as Pianykh et al. 2020
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Self-citation
Metadata
- Authors
- Pianykh, O. S., Langs, G., Dewey, M., Enzmann, D. R., Herold, C. J., Schoenberg, S. O., & Brink, J. A.
- Affiliations
- Harvard University (US)
- ORCID
- Venue
- Radiology
- Published
- 2020-10
- Volume
- 297
- Issue
- 1
- Pages
- 6-14
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- OpenAlex
- W3081038317
- Cited by
- 237
- References
- 39
- Citations by year
- 2026: 26 · 2025: 52 · 2024: 66 · 2023: 30 · 2022: 33 · 2021: 27 · 2020: 2
- FWCI
- 1.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Radiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- artificial intelligence in radiology, AI implementation, knowledge discovery from data, dynamic model updating, radiology automation
- MeSH terms
- Big Data, Artificial Intelligence, Humans, Radiology
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