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Current Applications and Future Impact of Machine Learning in Radiology
- Source :
- Radiology. 288:318-328
- Publication Year :
- 2018
- Publisher :
- Radiological Society of North America (RSNA), 2018.
-
Abstract
- Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting. In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology. In addition, the future impact and natural extension of these techniques in radiology practice are discussed. © RSNA, 2018
- Subjects :
- medicine.medical_specialty
media_common.quotation_subject
Radiology workflow
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Order scheduling
Machine learning
computer.software_genre
Clinical decision support system
030218 nuclear medicine & medical imaging
Machine Learning
03 medical and health sciences
0302 clinical medicine
Dose estimation
medicine
Medical imaging
Humans
Radiology, Nuclear Medicine and imaging
Quality (business)
media_common
business.industry
Triage
Reviews and Commentary
Radiology Information Systems
030220 oncology & carcinogenesis
Radiology
Artificial intelligence
Radiology information systems
business
computer
Subjects
Details
- ISSN :
- 15271315 and 00338419
- Volume :
- 288
- Database :
- OpenAIRE
- Journal :
- Radiology
- Accession number :
- edsair.doi.dedup.....eb5f8aa1ff318ca2247422ed3f5509f9