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Related Experiment Video

Updated: Jun 27, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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Evaluating Explanations From AI Algorithms for Clinical Decision-Making: A Social Science-Based Approach.

Suparna Ghanvatkar, Vaibhav Rajan

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    Summary
    This summary is machine-generated.

    We developed a new metric to evaluate the usefulness of AI explanations for clinicians. This tool helps select the most helpful AI-driven clinical decision support systems by assessing explanation relevance.

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    Area of Science:

    • Artificial Intelligence
    • Clinical Decision Support Systems
    • Explainable AI (XAI)

    Background:

    • Explainable Artificial Intelligence (XAI) provides reasons for AI model predictions.
    • Evaluating AI explanations involves assessing faithfulness and usefulness.
    • Automated metrics for explanation usefulness in clinical settings are lacking.

    Purpose of the Study:

    • To develop a novel metric for evaluating the usefulness of AI explanations for clinicians.
    • To create a scoring method considering human cognition and clinical requirements for AI explanations.

    Main Methods:

    • Developed a scoring method for XAI explanations providing feature importance values.
    • Integrated social science theories and biomedical knowledge graphs for usefulness assessment.
    • Evaluated the method using a case study on sepsis onset prediction in ICUs.

    Main Results:

    • The new metric's scores align with clinical literature evidence.
    • The developed metric demonstrates qualities suitable for evaluating AI explanation usefulness.
    • The method successfully quantifies the support for explanations from clinical literature.

    Conclusions:

    • The proposed metric can evaluate and select useful AI explanations in clinical contexts.
    • This tool is fundamental for designing effective AI-driven clinical decision support systems.
    • Facilitates the advancement of trustworthy and clinically relevant AI in healthcare.