The human factor in explainable artificial intelligence: clinician variability in trust, reliance, and performance
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View abstract on PubMed
Summary
This summary is machine-generated.Explainable Artificial Intelligence (XAI) in healthcare did not significantly improve clinician trust or reliance, despite reducing errors in gestational age estimation. User variability highlights the need for human studies in XAI deployment.
Area Of Science
- Medical Imaging
- Artificial Intelligence
- Human-Computer Interaction
Background
- Explainable Artificial Intelligence (XAI) is crucial for high-risk fields like healthcare to build user trust.
- Current evaluations often use automated metrics, neglecting crucial user-centered assessments.
Purpose Of The Study
- To evaluate the impact of a prototype-based XAI model on clinician trust, reliance, and performance in image-based gestational age estimation.
- To introduce and assess a novel metric for appropriate reliance on AI in clinical settings.
Main Methods
- A prototype-based XAI model was adapted for gestational age estimation from medical images.
- Ten sonographers participated in a three-stage reader study to assess the XAI model's effects.
- Clinician performance, trust, reliance, and confidence were measured, including a new metric for appropriate reliance.
Main Results
- The XAI model reduced the mean absolute error (MAE) in gestational age estimates from 23.5 to 15.7 days.
- Explanations provided by the XAI model led to a further, though not statistically significant, reduction in MAE to 14.3 days.
- Participant responses to explanations varied, with some performing worse; confidence increased, but trust and reliance remained unaffected.
Conclusions
- Explainable Artificial Intelligence (XAI) deployment in clinical settings requires careful consideration of user variability and its impact on performance.
- Automated metrics are insufficient; human studies are essential to understand the nuanced effects of XAI on clinician behavior and decision-making.
- The findings underscore potential challenges in achieving desired trust and reliance outcomes with current XAI approaches in medical applications.
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