Current Clinical Applications of Artificial Intelligence in Radiology and Their Best Supporting Evidence

Amara Tariq, Saptarshi Purkayastha, Geetha Priya Padmanaban, Elizabeth Krupinski, Hari Trivedi, Imon Banerjee, Judy Wawira Gichoya

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1371-1381
Number of pages11
JournalJournal of the American College of Radiology
Volume17
Issue number11
DOIs
StatePublished - Nov 2020
Externally publishedYes

Keywords

  • AI in clinical practice
  • open-source AI tools for radiology
  • proprietary AI tools for radiology
  • radiology image processing
  • survey of AI-based diagnostic tools

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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