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AI for radiographic COVID-19 detection selects shortcuts over signal.
DeGrave, Alex J; Janizek, Joseph D; Lee, Su-In.
  • DeGrave AJ; Paul G. Allen School of Computer Science and Engineering, University of Washington.
  • Janizek JD; Medical Scientist Training Program, University of Washington.
  • Lee SI; Paul G. Allen School of Computer Science and Engineering, University of Washington.
medRxiv ; 2020 Oct 07.
Article in English | MEDLINE | ID: covidwho-806998
Preprint
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ABSTRACT
Artificial intelligence (AI) researchers and radiologists have recently reported AI systems that accurately detect COVID-19 in chest radiographs. However, the robustness of these systems remains unclear. Using state-of-the-art techniques in explainable AI, we demonstrate that recent deep learning systems to detect COVID-19 from chest radiographs rely on confounding factors rather than medical pathology, creating an alarming situation in which the systems appear accurate, but fail when tested in new hospitals. We observe that the approach to obtain training data for these AI systems introduces a nearly ideal scenario for AI to learn these spurious "shortcuts." Because this approach to data collection has also been used to obtain training data for detection of COVID-19 in computed tomography scans and for medical imaging tasks related to other diseases, our study reveals a far-reaching problem in medical imaging AI. In addition, we show that evaluation of a model on external data is insufficient to ensure AI systems rely on medically relevant pathology, since the undesired "shortcuts" learned by AI systems may not impair performance in new hospitals. These findings demonstrate that explainable AI should be seen as a prerequisite to clinical deployment of ML healthcare models.

Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study Language: English Year: 2020 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study Language: English Year: 2020 Document Type: Article