Machine learning for medical imaging
Open accessAbstract
Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. ©RSNA, 2017
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Medical imaging data from radiology and pathology enable deep learning models to learn diagnostic and prognostic patterns from pixel-level representations.
“Medical imaging data from radiology and pathology enable deep learning models to learn diagnostic and prognostic patterns from pixel-level representations [6,29].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [6]
The Introduction describes diagnostic uses of radiologic images, and the Deep Learning section explains that CNNs learn important image features rather than requiring them to be computed first. The Definitions section says image features can include actual pixel values. Our copy does not establish the claim’s extension to pathology images or prognostic patterns. (LLM, conf=0.87)
Experimental — accuracy not yet measuredAI has demonstrated promising but variable potential in diagnostic decision-making, particularly in imaging-based applications that rely on deep learning to process high-dimensional visual data.
“AI has demonstrated promising but variable potential in diagnostic decision-making, particularly in imaging-based applications that rely on deep learning to process high-dimensional visual data [12,6].”
The widespread adoption of EHR systems has enabled the development of predictive models using longitudinal patient data.
“The widespread adoption of EHR systems has enabled the development of predictive models using longitudinal patient data [6,7].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [6]
The Introduction and Conclusion discuss machine learning for medical imaging, not the adoption of electronic health records. Searches for EHRs, electronic medical records, and longitudinal patient data found no passage linking them to the development of predictive models. (LLM, conf=0.75)
Experimental — accuracy not yet measuredMachine learning allows identification of patterns seen in previous cases and experiments, as occurs with human intelligence.
“Machine learning allows the identification of patterns seen in previous cases and experiments, as occurs with human intelligence(29).”
From Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine (2019) · cites this paper as [29]
Machine learning methods have been applied to classifying images acquired with various imaging modalities, using a variety of features, for various diseases, and with tools such as CAD and radiomics.
“Machine learning methods have been applied to classifying images acquired with various imaging modalities, using a variety of features, for various diseases, and with tools such as CAD and radiomics(25,29,30).”
From Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine (2019) · cites this paper as [29]
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Nyre-Yu, M., Morris, E., Smith, M., Moss, B., & Smutz, C. (2022) · Proceedings 2022 Symposium on Usable Security · doi:10.14722/usec.2022.23014 · Cited by 15 · see citing works
Asadpour, V., Puttock, E. J., Getahun, D., Fassett, M. J., & Xie, F. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e13577 · Cited by 11 · see citing works
Patil, S. S., Jha, G. K., Mohsin, N., Patel, P., Ralte, L., & Shukla, A. K. (2026) · Cureus · doi:10.7759/cureus.109905
- Authors
- Erickson, B. J., Korfiatis, P., Akkus, Z., & Kline, T. L.
- Affiliations
- Mayo Clinic (US)
- Venue
- RadioGraphics
- Published
- 2017-03
- Volume
- 37
- Issue
- 2
- Pages
- 505-515
- ISSN
- 0271-5333 (ISSN-L)
- 1527-1323
- PMC
- PMC5375621
- OpenAlex
- W2588978745
- Semantic Scholar
- 741b11606c2fb3167c756b3d8fd2d39e060b11f9
- Cited by
- 1,672
- References
- 59
- Citations by year
- 2026: 111 · 2025: 191 · 2024: 209 · 2023: 277 · 2022: 214 · 2021: 262 · 2020: 180 · 2019: 137 · 2018: 63 · 2017: 23 · 2015: 2 · 2014: 1 · 2012: 1
- FWCI
- 16.68
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Radiographics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Cancer Institute (CA160045, DK90728)
- PKD Foundation (206g16a)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- machine learning, diagnostic imaging, image classification, image feature extraction, deep learning, performance indicators, diagnosis
- MeSH terms
- Machine Learning, Algorithms, Diagnostic Imaging, Humans, Image Interpretation, Computer-Assisted
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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