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Machine Learning for the Interventional Radiologist.

Ryan D Meek¹, Matthew P Lungren², Judy W Gichoya¹

¹Charles T. Dotter Department of Interventional Radiology, Oregon Health & Science University, 3181 SW Sam Jackson Park Rd, Portland, OR 97239-3011.

AJR. American Journal of Roentgenology

|June 6, 2019

Summary

This summary is machine-generated.

Machine learning offers significant potential in interventional radiology for image analysis and predictive modeling. Early adoption by radiologists and trainees is crucial to guide the future of artificial intelligence in the field.

Keywords:

artificial intelligence interventional radiology machine learning

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Area of Science:

Medical Imaging
Artificial Intelligence
Interventional Radiology

Background:

Interventional radiology is a rapidly evolving medical specialty.
The integration of advanced technologies like machine learning is becoming increasingly important.

Purpose of the Study:

To outline the primary potential applications of machine learning within interventional radiology.
To highlight the current developmental stage and future prospects of machine learning in this field.

Main Methods:

Literature review and conceptual analysis of machine learning applications.
Discussion of potential impacts on clinical practice and education.

Main Results:

Machine learning shows promise in enhancing image analysis capabilities.
Potential exists for developing robust clinical predictive models.
Machine learning can significantly impact the education and training of interventional radiology professionals.

Conclusions:

Machine learning is in its nascent stages within interventional radiology but holds substantial promise.
Proactive engagement from interventional radiologists and trainees is essential to steer the development of machine learning and artificial intelligence.
The future integration of AI in interventional radiology requires strategic planning and active participation from practitioners.