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  11. Explainable Ai For Clinical Outcome Prediction: A Survey Of Clinician Perceptions And Preferences

Explainable AI for Clinical Outcome Prediction: A Survey of Clinician Perceptions and Preferences

Jun Hou1, Lucy Lu Wang2

  • 1Virginia Tech, Blacksburg, VA.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|June 12, 2025

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View abstract on PubMed

Summary
This summary is machine-generated.

Clinicians prefer different Explainable AI (XAI) techniques for interpreting AI predictions from electronic health records (EHR). Findings guide the selection of appropriate XAI methods for clinical decision-making.

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Decision Support

Background:

  • Explainable AI (XAI) is crucial for integrating AI predictions into clinical workflows.
  • Clinician trust and understanding of AI outputs depend on interpretable methods.
  • Text-based Electronic Health Record (EHR) data presents unique challenges for AI interpretation.

Purpose of the Study:

  • To assess clinician preferences for various XAI techniques applied to EHR data.
  • To understand how different XAI methods impact the interpretation of AI-driven mortality predictions.
  • To provide guidance on selecting and improving XAI tools for clinical practice.

Main Methods:

  • Implemented four XAI techniques: LIME, Attention-based span highlights, exemplar patient retrieval, and LLM-generated rationales.

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  • Developed an outcome prediction model using ICU admission notes to predict in-hospital mortality.
  • Conducted a survey study with 32 practicing clinicians to gather feedback on XAI technique preferences.

    • Main Results:

    • Identified varying clinician preferences for the four evaluated XAI techniques.
    • Gathered qualitative feedback on the usability and interpretability of each XAI method.
    • Highlighted specific scenarios where certain XAI techniques were favored over others.

    Conclusions:

    • Clinician preferences for XAI techniques differ based on the specific application and data type.
    • Findings inform the development of more effective and user-centered XAI tools for healthcare.
    • Recommendations are provided for optimizing XAI technique selection and implementation in clinical settings.