Clinical AI tools must convey predictive uncertainty for each individual patient
Clinical AI tools must convey predictive uncertainty for each individual patient
Artificial intelligence tools usually aim to maximize predictive accuracy, but personalized measures of uncertainty, using new techniques such as conformal prediction, are needed for clinical artificial intelligence to realize its potential and improve human health.
2996-2998
Banerji, Christopher R.S.
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Chakraborti, Tapabrata
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Harbron, Chris
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MacArthur, Ben D.
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December 2023
Banerji, Christopher R.S.
1f2450d6-5772-46b5-a913-2333f7b53a2a
Chakraborti, Tapabrata
26a5ab6f-fd15-4be2-bc8b-ed53f8913548
Harbron, Chris
c9053c59-3f33-4842-aead-905f4a5b20ec
MacArthur, Ben D.
2c0476e7-5d3e-4064-81bb-104e8e88bb6b
Banerji, Christopher R.S., Chakraborti, Tapabrata, Harbron, Chris and MacArthur, Ben D.
(2023)
Clinical AI tools must convey predictive uncertainty for each individual patient.
Nature Medicine, 29 (12), .
(doi:10.1038/s41591-023-02562-7).
Abstract
Artificial intelligence tools usually aim to maximize predictive accuracy, but personalized measures of uncertainty, using new techniques such as conformal prediction, are needed for clinical artificial intelligence to realize its potential and improve human health.
Text
Nat Med
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Available under License Other.
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e-pub ahead of print date: 11 October 2023
Published date: December 2023
Identifiers
Local EPrints ID: 484397
URI: http://eprints.soton.ac.uk/id/eprint/484397
ISSN: 1078-8956
PURE UUID: 941ca686-1b38-45d2-8fe7-b3e2a40ac079
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Date deposited: 16 Nov 2023 11:49
Last modified: 10 Aug 2024 04:01
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Contributors
Author:
Christopher R.S. Banerji
Author:
Tapabrata Chakraborti
Author:
Chris Harbron
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