2022
DOI: 10.21203/rs.3.rs-2231672/v1
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Enhancing the reliability and accuracy of AI-enabled diagnosis via complementarity-driven deferral to clinicians (CoDoC)

Abstract: Diagnostic AI systems trained using deep learning have been shown to achieve expert-level identification of diseases in multiple medical imaging settings1,2. 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 discordant decisions of AIs and clinicians have remained largely unexplored in healthcare4, yet have the potential to achieve levels of performance th… Show more

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Cited by 4 publications
(4 citation statements)
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“…Clinician-AI collaboration typically becomes unhelpful when the experts overly rely on the AI predictions (Rajpurkar et al, 2020;Seah et al, 2021) or are unduly critical about them (Agarwal et al, 2023). Development of strategies for identifying when to provide AI generated reports is likely to be helpful for maximising the benefits of AI-assistance (Dvijotham et al, 2023). Fourth, while it is plausible that revising an AI-generated report may require less time than composing one from scratch, this work does not assess this explicitly and it is beyond the scope of the current work.…”
Section: Discussionmentioning
confidence: 99%
“…Clinician-AI collaboration typically becomes unhelpful when the experts overly rely on the AI predictions (Rajpurkar et al, 2020;Seah et al, 2021) or are unduly critical about them (Agarwal et al, 2023). Development of strategies for identifying when to provide AI generated reports is likely to be helpful for maximising the benefits of AI-assistance (Dvijotham et al, 2023). Fourth, while it is plausible that revising an AI-generated report may require less time than composing one from scratch, this work does not assess this explicitly and it is beyond the scope of the current work.…”
Section: Discussionmentioning
confidence: 99%
“…On the other hand, for diagnostic tasks, the emphasis may be on high specificity to minimize false positives, providing more confidence in the accuracy of the identified cases. The flexibility to adjust the operating point allows healthcare professionals or system administrators to align the AI model with the specific goals of the task at hand (50).…”
Section: Discussionmentioning
confidence: 99%
“…Deep learning (DL), a sub specialization of ML, has been successfully applied to genomics 3 and imaging data for diagnostic applications. 4 Radiomics 5-7 is a burgeoning field that uses algorithms to extract high throughput features from medical images using either engineered methods (i.e., using predefined recipes) or models that are trained end-to-end. Radiomics has the potential to uncover disease features and characteristics that cannot be appreciated by the naked eye.…”
Section: Value Of Ai/ml Analysismentioning
confidence: 99%
“…As compared with traditional statistical modeling, state‐of‐the‐art ML algorithms may offer the advantages of superior predictive performance (e.g., treatment response prediction) and the ability to handle high‐dimensional data (such as medical imaging or large genomics arrays). Deep learning (DL), a sub specialization of ML, has been successfully applied to genomics 3 and imaging data for diagnostic applications 4 . Radiomics 5–7 is a burgeoning field that uses algorithms to extract high throughput features from medical images using either engineered methods (i.e., using predefined recipes) or models that are trained end‐to‐end.…”
Section: Introductionmentioning
confidence: 99%