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Current Applications and Future Impact of Machine Learning in Radiology
Garry Choy1, Omid Khalilzadeh1, Mark Michalski1
1From the Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, Mass 02114 (G.C., O.K., M.M., S.D., A.E.S., O.S.P., P.V.P., J.A.B., K.J.D.); and Department of Radiology, University of Colorado School of Medicine, Aurora, Colo (J.R.G.).
Radiology
|June 27, 2018
Summary
Machine learning and artificial intelligence show great promise for improving medical imaging and radiology workflows. These advanced techniques can enhance everything from scheduling and diagnosis to reporting and quality control.
Area of Science:
- Radiology
- Medical Imaging
- Artificial Intelligence
- Machine Learning
Background:
- Machine learning (ML) and artificial intelligence (AI) are rapidly advancing, presenting significant opportunities in medical imaging.
- These technologies have the potential to revolutionize various aspects of the radiology workflow.
Purpose of the Study:
- To review current applications of ML and AI in diagnostic radiology.
- To discuss the future impact and integration of these techniques into radiology practice.
Main Methods:
- Literature review of current ML and AI applications in diagnostic radiology.
- Discussion of future perspectives and natural extensions of these technologies.
Main Results:
- ML/AI can enhance radiology workflows, including scheduling, triage, clinical decision support, and detection/interpretation.
- Applications extend to postprocessing, dose estimation, quality control, and radiology reporting.
Conclusions:
- ML and AI are poised to significantly impact and transform diagnostic radiology.
- Continued development and integration of these techniques will be crucial for future advancements in the field.
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