Radiologist’s Guide to Evaluating Publications of Clinical Research on AI: How We Do It
Author:
Funder
National Evidence-based Healthcare Collaborating Agency
Publisher
Radiological Society of North America (RSNA)
Subject
Radiology, Nuclear Medicine and imaging
Link
http://pubs.rsna.org/doi/pdf/10.1148/radiol.230288
Reference27 articles.
1. Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis
2. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis
3. Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers
4. How to Read Articles That Use Machine Learning
5. A Clinician's Guide to Artificial Intelligence: How to Critically Appraise Machine Learning Studies
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1. Conceptual review of outcome metrics and measures used in clinical evaluation of artificial intelligence in radiology;La radiologia medica;2024-09-03
2. Survey on Value Elements Provided by Artificial Intelligence and Their Eligibility for Insurance Coverage With an Emphasis on Patient-Centered Outcomes;Korean Journal of Radiology;2024
3. Reporting Guidelines for Artificial Intelligence Studies in Healthcare (for Both Conventional and Large Language Models): What’s New in 2024;Korean Journal of Radiology;2024
4. Reporting Quality of Research Studies on AI Applications in Medical Images According to the CLAIM Guidelines in a Radiology Journal With a Strong Prominence in Asia;Korean Journal of Radiology;2023
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