What question did this study set out to answer?

The aim is to explore how effectively communicating uncertainty can enhance decision-making in medical AI applications.

January 5, 2021npj Digital MedicineOpen Access

Second opinion needed: communicating uncertainty in medical machine learning

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Overview discusses how uncertainty quantification improves safety in healthcare machine learning models, enhancing trust among workers.

The aim is to explore how effectively communicating uncertainty can enhance decision-making in medical AI applications.
Review of various approaches to uncertainty quantification in machine learning.
Analysis of methods for abstention in uncertain predictions.
Discussion on the implications of these techniques in healthcare settings.
Highlighting that better uncertainty communication could foster trust among healthcare professionals.
Identifying safeguards provided by uncertainty measures against machine learning failures.
Emphasizing that acknowledging uncertainty is crucial for safe deployment in clinical environments.

Kompa et al. (2021) studied this question.

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