Second opinion needed: communicating uncertainty in medical machine learning.

Area of Science:

• Medical Artificial Intelligence
• Machine Learning in Healthcare
• Clinical Decision Support Systems

Background:

• Machine learning (ML) shows promise for enhancing patient-level decision-making in healthcare.
• Current ML applications often neglect the quantification and communication of prediction uncertainty.
• This omission hinders principled decision-making and limits the ability of models to abstain from uncertain predictions.

Purpose of the Study:

• To provide an overview of uncertainty quantification and abstention methods for ML in healthcare.
• To highlight how these techniques can improve the safety and reliability of medical AI.
• To emphasize the importance of uncertainty communication for clinical trust and deployment.

Main Methods:

• Review of various approaches to uncertainty quantification in machine learning.
• Discussion of abstention mechanisms for ML models.
• Analysis of the impact of uncertainty estimates on clinical decision-making.

Main Results:

• Effective uncertainty quantification enables more principled ML-driven decisions.
• Abstention on high-uncertainty samples enhances model reliability.
• Communicating uncertainty can foster trust between healthcare workers and AI systems.

Conclusions:

• Uncertainty quantification and communication are vital for safe and reliable medical AI.
• These methods provide safeguards against known ML failure modes in clinical settings.
• The ability for AI to express uncertainty is essential for its integration into healthcare environments.