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A Visual Analytics Framework for Assessing Interactive AI for Clinical Decision Support.

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Evaluating artificial intelligence (AI) in clinical decision-making is complex. This study introduces a visual analytics framework to analyze evaluation data, aiding in subgroup identification and regulatory compliance for AI tools.

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Area of Science:

  • Clinical Informatics
  • Artificial Intelligence in Medicine
  • Health Data Analytics

Background:

  • Human oversight is crucial in clinical decision-making, prompting the development of AI systems to assist clinicians.
  • Evaluating the impact of AI on patient care and workflows necessitates rigorous studies, which are often challenging and time-consuming.
  • Previous work established a toolkit for designing and evaluating clinical AI software.

Purpose of the Study:

  • To present a novel visual analytics framework for analyzing and interpreting data from evaluations of AI-supported clinical decision-making systems.
  • To facilitate the identification of user and patient subgroups, outlier detection, and assessment of regulatory guideline adherence.
  • To demonstrate the framework's utility in evaluating AI clinical decision-support tools.

Main Methods:

  • Development of a visual analytics framework tailored for AI evaluation data.
  • Integration of multiple-factor analysis and hierarchical clustering for data interpretation.
  • Utilization of interactive visualizations for exploring analytical results.
  • Guidance from early-stage clinical AI regulatory guidelines in framework design.

Main Results:

  • The framework enables the identification of distinct user and patient subgroups based on characteristics.
  • It facilitates the detection of outliers within these subgroups.
  • The system provides evidence supporting adherence to regulatory guidelines.
  • A case study demonstrated the framework's effectiveness in evaluating an AI system for pediatric brain tumor diagnosis.

Conclusions:

  • The visual analytics framework offers a structured approach to analyzing complex data from AI clinical evaluation studies.
  • It enhances the interpretability of AI system performance and its impact on clinical practice.
  • The framework supports evidence-based decision-making for the implementation and regulation of AI in healthcare.