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A No-Math Primer on the Principles of Machine Learning for Radiologists
Matthew D Lee1, Mohammed Elsayed1, Sumit Chopra2
1Department of Radiology, NYU Grossman School of Medicine, New York, NY.
Machine learning (ML) and deep learning are vital in radiology research and clinical practice. This primer explains fundamental ML concepts for radiologists and trainees to understand these transformative technologies.
Area of Science:
- Radiology
- Medical Imaging
- Artificial Intelligence
Background:
- Machine learning (ML) is rapidly advancing, impacting radiology research and clinical applications.
- Deep learning, a subset of ML, drives many recent technological developments in medical imaging.
Purpose of the Study:
- To provide radiologists and trainees with a foundational understanding of ML principles.
- To introduce key vocabulary and concepts relevant to ML in radiology.
Main Methods:
- This work is a primer, explaining core ML concepts.
- Focuses on accessibility for a clinical audience.
Main Results:
- N/A - This is a primer, not an experimental study.
- N/A
Conclusions:
- Understanding ML is crucial for radiologists navigating technological advancements.
- This primer serves as an accessible educational resource for ML in radiology.
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