Journal
JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY
Volume 17, Issue 11, Pages 1371-1381Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jacr.2020.08.018
Keywords
AI in clinical practice; radiology image processing; survey of AI-based diagnostic tools; open-source AI tools for radiology; proprietary AI tools for radiology
Funding
- National Science Foundation, Division of Electrical, Communication and Cyber Systems [1928481]
- National Science Foundation
- Directorate For Engineering
- Div Of Electrical, Commun & Cyber Sys [1928481] Funding Source: National Science Foundation
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Purpose: Despite tremendous gains from deep learning and the promise of artificial intelligence (AI) in medicine to improve diagnosis and save costs, there exists a large translational gap to implement and use AI products in real-world clinical situations. Adoption of standards such as Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis, Consolidated Standards of Reporting Trials, and the Checklist for Artificial Intelligence in Medical Imaging is increasing to improve the peer-review process and reporting of AI tools. However, no such standards exist for product-level review. Methods: A review of clinical trials showed a paucity of evidence for radiology AI products; thus, the authors developed a 10-question assessment tool for reviewing AI products with an emphasis on their validation and result dissemination. The assessment tool was applied to commercial and open-source algorithms used for diagnosis to extract evidence on the clinical utility of the tools. Results: There is limited technical information on methodologies for FDA-approved algorithms compared with open-source products, likely because of intellectual property concerns. Furthermore, FDA-approved products use much smaller data sets compared with opensource AI tools, because the terms of use of public data sets are limited to academic and noncommercial entities, which precludes their use in commercial products. Conclusions: Overall, this study reveals a broad spectrum of maturity and clinical use of AI products, but a large gap exists in exploring actual performance of AI tools in clinical practice.
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