Jul 17, 2023 · Determining when predictive AI or a clinician is more accurate. With CoDoC, we propose a simple and usable AI system to improve reliability by helping predictive AI systems to ‘know when they don’t know’.

This repository includes the source code for the paper "Enhancing the reliability and accuracy of AI-enabled diagnosis via complementarity-driven deferral to clinicians (CoDoC)" by Dvijotham et al. (2023), published in the journal Nature Medicine.

Jul 17, 2023 · We developed Complementarity-Driven Deferral to Clinical Workflow (CoDoC), a system that can learn to decide between the opinion of a predictive AI model and a clinical workflow. CoDoC...

Jul 19, 2023 · But new research unveiled in a joint paper by Google DeepMind and Google Research proposes a promising solution. The research presents Complementarity-driven Deferral-to-Clinical Workflow (CoDoC), an AI model that learns when to trust a predictive AI's diagnosis or defer to a clinician.

Jul 25, 2023 · CoDoC is an AI system that determines when to rely on predictive AI models and when to defer to human clinicians for accurate medical image interpretation. By combining …

Explore CoDoC by DeepMind: The groundbreaking AI-human collaboration transforming medical imaging, ensuring optimal patient care. In today's rapidly evolving technological landscape, the …

Jul 17, 2023 · In response, Google has developed a new AI system called the Complementarity-driven Deferral-to-Clinical Workflow (CoDoC), which can discern when to trust AI-based diagnoses and prompt a second opinion from a human doctor.

The CoDoC (Complementarity-Driven Deferral to Clinical Workflow) framework represents a significant stride in designing AI that harmonizes with clinical operations, particularly in the realm of medical image analysis.

Aug 7, 2023 · In a world where technology and medicine intertwined, a revolutionary AI system emerged from the labs of Google. This extraordinary creation was named Complementarity-driven Deferral-to-Clinical Workflow, or simply CoDoC.

This repository includes the source code for the paper "Enhancing the reliability and accuracy of AI-enabled diagnosis via complementarity-driven deferral to clinicians (CoDoC)" by Dvijotham et al. (2023), published in the journal Nature Medicine.