Artificial intelligence for classification of lung nodules a review of clinical utility, diagnostic accuracy, cost-effectiveness, and guidelines
Seven diagnostic case-control studies were identified regarding the diagnostic accuracy of artificial intelligence for nodule classification in screening, incidental identification, or known or suspected malignancies for lung cancer. No evidence regarding the cost-effectiveness, clinical utility or...
| Main Authors: | , |
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| Corporate Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
Ottawa
Canadian Agency for Drugs and Technologies in Health
2020, January 22, 2020
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| Edition: | Version: 1.0 |
| Series: | CADTH rapid response report: summary with critical appraisal
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| Subjects: | |
| Online Access: | |
| Collection: | National Center for Biotechnology Information - Collection details see MPG.ReNa |
| Summary: | Seven diagnostic case-control studies were identified regarding the diagnostic accuracy of artificial intelligence for nodule classification in screening, incidental identification, or known or suspected malignancies for lung cancer. No evidence regarding the cost-effectiveness, clinical utility or evidence-based guidelines regarding artificial intelligence for nodule classification in screening, incidental identification, or known or suspected malignancies for lung cancer were identified |
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| Physical Description: | 1 PDF file (23 pages) illustrations |