When Machines Think: Radiology’s Next Frontier
Abstract
Artificial intelligence (AI), machine learning, and deep learning are terms now seen frequently, all of which refer to computer algorithms that change as they are exposed to more data. Many of these algorithms are surprisingly good at recognizing objects in images. The combination of large amounts of machine-consumable digital data, increased and cheaper computing power, and increasingly sophisticated statistical models combine to enable machines to find patterns in data in ways that are not only cost-effective but also potentially beyond humans’ abilities. Building an AI algorithm can be surprisingly easy. Understanding the associated data structures and statistics, on the other hand, is often difficult and obscure. Converting the algorithm into a sophisticated product that works consistently in broad, general clinical use is complex and incompletely understood. To show how these AI products reduce costs and improve outcomes will require clinical translation and industrial-grade integration into routine workflow. Radiology has the chance to leverage AI to become a center of intelligently aggregated, quantitative, diagnostic information. Centaur radiologists, formed as a synergy of human plus computer, will provide interpretations using data extracted from images by humans and image-analysis computer algorithms, as well as the electronic health record, genomics, and other disparate sources. These interpretations will form the foundation of precision health care, or care customized to an individual patient. © RSNA, 2017
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Productizing AI algorithms for clinical use is an exceptionally complex task.
“Beyond developing AI algorithms, productizing them for clinical use is an exceptionally complex task4,13.”
From Establishing organizational AI governance in healthcare: a case study in Canada (2025) · cites this paper as [13]
AI adoption in healthcare is expected to grow, but its actual implementation currently remains limited due to a variety of challenges.
“While AI adoption is expected to grow, its actual implementation currently remains limited due to a variety of challenges.”
From Establishing organizational AI governance in healthcare: a case study in Canada (2025) · cites this paper as [13]
AI is expected to augment healthcare workflow through automated triage, improve the productivity of individual physicians, reduce human errors, discover better patterns of patient care, defray medical costs, perform minimally invasive surgery, and reduce mortality rates.
“AI is expected to augment healthcare workflow through automated triage, improve the productivity of individual physicians, reduce human errors, discover better patterns of patient care, defray medical costs, perform minimally invasive surgery, and reduce mortality rates.8–14”
From Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes (2021) · cites this paper as [9]
Implementation of AI in clinical radiology practice is complex and has so far been slow.
“While technical performance of AI applications is expected to increase continuously, its implementation in clinical radiology practice is rather complex and has so far been slow [6, 12, 13].”
From Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors (2020) · cites this paper as [12]
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- Authors
- Dreyer, K. J., & Geis, J. R.
- Affiliations
- Massachusetts General Hospital (US)
- Venue
- Radiology
- Published
- 2017-12
- Volume
- 285
- Issue
- 3
- Pages
- 713-718
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- OpenAlex
- W2770869174
- Cited by
- 183
- References
- 4
- Citations by year
- 2026: 12 · 2025: 16 · 2024: 22 · 2023: 17 · 2022: 24 · 2021: 24 · 2020: 20 · 2019: 34 · 2018: 13 · 2017: 1
- FWCI
- 10.86
- 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 › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- artificial intelligence in radiology, machine learning, deep learning, computer-aided diagnosis, clinical translation of AI, human-AI collaboration, electronic health record integration, image analysis algorithms
- MeSH terms
- Machine Learning, Algorithms, Diagnostic Imaging, Forecasting, Humans, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Radiology, Software, Decision Support Systems, Clinical
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