Enhancing the reliability and accuracy of AI-enabled diagnosis via complementarity-driven deferral to clinicians (CoDoC)

Abstract excerpt

<title>Abstract</title> <p>Diagnostic AI systems trained using deep learning have been shown to achieve expert-level identification of diseases in multiple medical imaging settings^1,2. However, such systems are not always reliable and can fail in cases diagnosed accurately by clinicians and vice versa³. Mechanisms for leveraging this complementarity by learning to select optimally between di...

Topics

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• AI in cancer detection
• Artificial Intelligence
• Cancer Genomics and Diagnostics
• Genetic Variation
• Image Processing, Computer-Assisted
• Neoplasms
• Pancreatic and Hepatic Oncology Research
• Radiomics and Machine Learning in Medical Imaging

Identifiers and source

Literature Corpus work

b106bf51-fcba-5216-91d1-3e57e4f125d2

DOI

10.21203/rs.3.rs-2231672/v1