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Experimental evidence of effective human–AI collaboration in medical decision-making

Artificial Intelligence (ai) systems are precious support for decision-making, with many applications also in the medical domain. The interaction between mds and ai enjoys a renewed interest following the increased possibilities of deep learning devices. However, we still have limited evidence-based...

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Autores principales: Reverberi, Carlo, Rigon, Tommaso, Solari, Aldo, Hassan, Cesare, Cherubini, Paolo, Cherubini, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9440124/
https://www.ncbi.nlm.nih.gov/pubmed/36056152
http://dx.doi.org/10.1038/s41598-022-18751-2
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author Reverberi, Carlo
Rigon, Tommaso
Solari, Aldo
Hassan, Cesare
Cherubini, Paolo
Cherubini, Andrea
author_facet Reverberi, Carlo
Rigon, Tommaso
Solari, Aldo
Hassan, Cesare
Cherubini, Paolo
Cherubini, Andrea
author_sort Reverberi, Carlo
collection PubMed
description Artificial Intelligence (ai) systems are precious support for decision-making, with many applications also in the medical domain. The interaction between mds and ai enjoys a renewed interest following the increased possibilities of deep learning devices. However, we still have limited evidence-based knowledge of the context, design, and psychological mechanisms that craft an optimal human–ai collaboration. In this multicentric study, 21 endoscopists reviewed 504 videos of lesions prospectively acquired from real colonoscopies. They were asked to provide an optical diagnosis with and without the assistance of an ai support system. Endoscopists were influenced by ai ([Formula: see text] ), but not erratically: they followed the ai advice more when it was correct ([Formula: see text] ) than incorrect ([Formula: see text] ). Endoscopists achieved this outcome through a weighted integration of their and the ai opinions, considering the case-by-case estimations of the two reliabilities. This Bayesian-like rational behavior allowed the human–ai hybrid team to outperform both agents taken alone. We discuss the features of the human–ai interaction that determined this favorable outcome.
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spelling pubmed-94401242022-09-04 Experimental evidence of effective human–AI collaboration in medical decision-making Reverberi, Carlo Rigon, Tommaso Solari, Aldo Hassan, Cesare Cherubini, Paolo Cherubini, Andrea Sci Rep Article Artificial Intelligence (ai) systems are precious support for decision-making, with many applications also in the medical domain. The interaction between mds and ai enjoys a renewed interest following the increased possibilities of deep learning devices. However, we still have limited evidence-based knowledge of the context, design, and psychological mechanisms that craft an optimal human–ai collaboration. In this multicentric study, 21 endoscopists reviewed 504 videos of lesions prospectively acquired from real colonoscopies. They were asked to provide an optical diagnosis with and without the assistance of an ai support system. Endoscopists were influenced by ai ([Formula: see text] ), but not erratically: they followed the ai advice more when it was correct ([Formula: see text] ) than incorrect ([Formula: see text] ). Endoscopists achieved this outcome through a weighted integration of their and the ai opinions, considering the case-by-case estimations of the two reliabilities. This Bayesian-like rational behavior allowed the human–ai hybrid team to outperform both agents taken alone. We discuss the features of the human–ai interaction that determined this favorable outcome. Nature Publishing Group UK 2022-09-02 /pmc/articles/PMC9440124/ /pubmed/36056152 http://dx.doi.org/10.1038/s41598-022-18751-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Reverberi, Carlo
Rigon, Tommaso
Solari, Aldo
Hassan, Cesare
Cherubini, Paolo
Cherubini, Andrea
Experimental evidence of effective human–AI collaboration in medical decision-making
title Experimental evidence of effective human–AI collaboration in medical decision-making
title_full Experimental evidence of effective human–AI collaboration in medical decision-making
title_fullStr Experimental evidence of effective human–AI collaboration in medical decision-making
title_full_unstemmed Experimental evidence of effective human–AI collaboration in medical decision-making
title_short Experimental evidence of effective human–AI collaboration in medical decision-making
title_sort experimental evidence of effective human–ai collaboration in medical decision-making
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9440124/
https://www.ncbi.nlm.nih.gov/pubmed/36056152
http://dx.doi.org/10.1038/s41598-022-18751-2
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