TY - JOUR
T1 - Current Clinical Applications of Artificial Intelligence in Radiology and Their Best Supporting Evidence
AU - Tariq, Amara
AU - Purkayastha, Saptarshi
AU - Padmanaban, Geetha Priya
AU - Krupinski, Elizabeth
AU - Trivedi, Hari
AU - Banerjee, Imon
AU - Gichoya, Judy Wawira
N1 - Publisher Copyright:
© 2020 American College of Radiology
PY - 2020/11
Y1 - 2020/11
N2 - 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 open-source 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.
AB - 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 open-source 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.
KW - AI in clinical practice
KW - open-source AI tools for radiology
KW - proprietary AI tools for radiology
KW - radiology image processing
KW - survey of AI-based diagnostic tools
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U2 - 10.1016/j.jacr.2020.08.018
DO - 10.1016/j.jacr.2020.08.018
M3 - Article
C2 - 33153541
AN - SCOPUS:85093107735
SN - 1546-1440
VL - 17
SP - 1371
EP - 1381
JO - Journal of the American College of Radiology
JF - Journal of the American College of Radiology
IS - 11
ER -