Clinical AI Needs More Transparency on Uncertainty to Improve Patient Care
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Clinical AI tools should provide measures of uncertainty for predictions to benefit individual patients.
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Uncertainty measures like conformal prediction can improve AI clinical utility.
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Predictive uncertainty is needed to judge AI reliability and determine appropriate actions.
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Lack of uncertainty estimates can limit clinical implementation of AI.
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Techniques exist to quantify uncertainty but are not widely adopted yet in clinical AI.
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