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Trust in Machine Learning Driven Clinical Decision Support Tools Among Otolaryngologists.

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Summary

Otolaryngologists are influenced by machine learning clinical decision support tools (ML-CDST), particularly when lacking diagnostic confidence. Specific explanations from ML-CDST increase physician trust and diagnostic judgment revision.

Keywords:
artificial intelligencelaryngologymachine learning

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Otolaryngology-Head & Neck Surgery

Background:

  • Machine learning-driven clinical decision support tools (ML-CDST) are nearing integration into clinical practice.
  • This study examines the impact of ML-CDST on diagnostic judgment within Otolaryngology-Head & Neck Surgery.

Purpose of the Study:

  • To investigate whether ML-CDST influence otolaryngologists' diagnostic decisions.
  • To assess the effect of ML-CDST explanation types on diagnostic judgment.

Main Methods:

  • Otolaryngologists participated in a virtual experiment involving human-AI interaction.
  • Participants diagnosed 12 video-stroboscopic exams and then revised diagnoses after ML-CDST output.
  • ML-CDST explanations varied: none, general, or specific logic.

Main Results:

  • Physicians were significantly more likely to change their diagnosis when reporting lower confidence (p=0.001).
  • A specific explanation of the ML-CDST logic increased diagnostic judgment revision (p=0.048).
  • Forty-five otolaryngologists were recruited for this study.

Conclusions:

  • Otolaryngologists demonstrate susceptibility to ML-CDST diagnostic recommendations, especially when uncertain.
  • Specific explanations enhance otolaryngologists' trust in ML-CDST outputs.
  • The explainability of AI tools is crucial for clinical adoption.