Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success
Abstract
Worldwide interest in artificial intelligence (AI) applications, including imaging, is high and growing rapidly, fueled by availability of large datasets ("big data"), substantial advances in computing power, and new deep-learning algorithms. Apart from developing new AI methods per se, there are many opportunities and challenges for the imaging community, including the development of a common nomenclature, better ways to share image data, and standards for validating AI program use across different imaging platforms and patient populations. AI surveillance programs may help radiologists prioritize work lists by identifying suspicious or positive cases for early review. AI programs can be used to extract "radiomic" information from images not discernible by visual inspection, potentially increasing the diagnostic and prognostic value derived from image datasets. Predictions have been made that suggest AI will put radiologists out of business. This issue has been overstated, and it is much more likely that radiologists will beneficially incorporate AI methods into their practices. Current limitations in availability of technical expertise and even computing power will be resolved over time and can also be addressed by remote access solutions. Success for AI in imaging will be measured by value created: increased diagnostic certainty, faster turnaround, better outcomes for patients, and better quality of work life for radiologists. AI offers a new and promising set of methods for analyzing image data. Radiologists will explore these new pathways and are likely to play a leading role in medical applications of AI.
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Training with diverse data from multiple sites without centralizing data can mitigate the problems of narrow training data.
“This can be mitigated by training with diverse data from multiple sites without centralising data33 using methods such as Transfer Learning34,35 or FL.”
From Federated learning for predicting clinical outcomes in patients with COVID-19 (2021) · cites this paper as [33]
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- Authors
- Thrall, J. H., Li, X., Li, Q., Cruz, C., Do, S., Dreyer, K., & Brink, J.
- Affiliations
- Harvard University (US)
- Corresponding author
- Thrall, James H.
- Venue
- Journal of the American College of Radiology
- Publisher
- Elsevier BV
- Published
- 2018-03
- Volume
- 15
- Issue
- 3
- Pages
- 504-508
- ISSN
- 1546-1440 (ISSN-L)
- OpenAlex
- W2785645041
- Cited by
- 710
- References
- 16
- Citations by year
- 2026: 38 · 2025: 72 · 2024: 117 · 2023: 98 · 2022: 125 · 2021: 114 · 2020: 70 · 2019: 60 · 2018: 16
- FWCI
- 16.97
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American College of 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, radiomics, radiologist role, clinical implementation of AI, image data sharing, nomenclature
- MeSH terms
- Machine Learning, Deep Learning, Big Data, Artificial Intelligence, Forecasting, Humans, Radiology
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