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> Say the model outputs 90% chance of nothing, 10% chance of a deadly condition that needs to be investigated ASAP. Doctors are often overworked and tired and can miss it. Knowing where the certainties and uncertainties of the model lies would allow the doctor to compare that to its own training "Ah but it didn't at all judge based on X and Y" and limit decision machine based on "trusting the algorithms".

Spot on. Personally, I always enjoy Alex Kendall and Yarin Gal's writing on the topic, for example:

https://alexgkendall.com/computer_vision/bayesian_deep_learn...




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