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Artificial intelligence and clinical decision support: clinicians’ perspectives on trust, trustworthiness, and liability

Caroline Jones Orcid Logo, James Thornton, Jeremy C Wyatt

Medical Law Review, Volume: 31, Issue: 4, Pages: 501 - 520

Swansea University Author: Caroline Jones Orcid Logo

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DOI (Published version): 10.1093/medlaw/fwad013

Abstract

Artificial intelligence (AI) could transform healthcare provision, possibly improving patient safety and clinician decision-making, and mitigating the effects of staff shortages. However, there are concerns - voiced by regulators and policy-makers - over whether AI and clinical decision support syst...

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Published in: Medical Law Review
ISSN: 0967-0742 1464-3790
Published: Oxford, UK Oxford University Press (OUP) 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa63456
first_indexed 2023-05-15T14:49:52Z
last_indexed 2024-11-15T18:01:37Z
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spelling 2024-06-05T16:19:51.9048711 v2 63456 2023-05-15 Artificial intelligence and clinical decision support: clinicians’ perspectives on trust, trustworthiness, and liability 8201817d55a832f7c23f406402904a2b 0000-0001-7632-9468 Caroline Jones Caroline Jones true false 2023-05-15 HRCL Artificial intelligence (AI) could transform healthcare provision, possibly improving patient safety and clinician decision-making, and mitigating the effects of staff shortages. However, there are concerns - voiced by regulators and policy-makers - over whether AI and clinical decision support systems (CDSSs) are trusted by relevant stakeholders, and more importantly whether such tools are worthy of trust. Yet, the meaning ascribed to trust and trustworthiness is often implicit, and it may be unclear what or who is being trusted. We address these issues, focusing for the most part on the perspective(s) of clinicians. Empirical studies suggest clinicians’ concerns about the use of AI/CDSSs include the accuracy of advice given and potential legal liability if a patient is harmed. Onora O’Neill’s conceptualisation of trust and trustworthiness provides the framework for our analysis. Through unpacking and reflecting upon these two concepts we gain greater clarity over the meaning given to them by a range of stakeholders; minimise the extent to/ways in which stakeholders are talking at cross purposes; and maintain the value of trust and trustworthiness as useful concepts in debates around the use of AI and CDSSs. Journal Article Medical Law Review 31 4 501 520 Oxford University Press (OUP) Oxford, UK 0967-0742 1464-3790 Artificial intelligence, Clinical decision support, Clinicians’ perspectives, Liability, Trust, Trustworthiness 27 11 2023 2023-11-27 10.1093/medlaw/fwad013 COLLEGE NANME Hillary Rodham Clinton Law School COLLEGE CODE HRCL Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2024-06-05T16:19:51.9048711 2023-05-15T15:14:42.6295952 Faculty of Humanities and Social Sciences Hilary Rodham Clinton School of Law Caroline Jones 0000-0001-7632-9468 1 James Thornton 2 Jeremy C Wyatt 3 63456__27604__98bbc46a9ae04b569e12f1d11bd171fa.pdf 63456.pdf 2023-05-24T16:08:41.6638949 Output 320713 application/pdf Version of Record true Distributed under the terms of a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). true eng https://creativecommons.org/licenses/by-nc-nd/4.0/
title Artificial intelligence and clinical decision support: clinicians’ perspectives on trust, trustworthiness, and liability
spellingShingle Artificial intelligence and clinical decision support: clinicians’ perspectives on trust, trustworthiness, and liability
Caroline Jones
title_short Artificial intelligence and clinical decision support: clinicians’ perspectives on trust, trustworthiness, and liability
title_full Artificial intelligence and clinical decision support: clinicians’ perspectives on trust, trustworthiness, and liability
title_fullStr Artificial intelligence and clinical decision support: clinicians’ perspectives on trust, trustworthiness, and liability
title_full_unstemmed Artificial intelligence and clinical decision support: clinicians’ perspectives on trust, trustworthiness, and liability
title_sort Artificial intelligence and clinical decision support: clinicians’ perspectives on trust, trustworthiness, and liability
author_id_str_mv 8201817d55a832f7c23f406402904a2b
author_id_fullname_str_mv 8201817d55a832f7c23f406402904a2b_***_Caroline Jones
author Caroline Jones
author2 Caroline Jones
James Thornton
Jeremy C Wyatt
format Journal article
container_title Medical Law Review
container_volume 31
container_issue 4
container_start_page 501
publishDate 2023
institution Swansea University
issn 0967-0742
1464-3790
doi_str_mv 10.1093/medlaw/fwad013
publisher Oxford University Press (OUP)
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str Hilary Rodham Clinton School of Law{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}Hilary Rodham Clinton School of Law
document_store_str 1
active_str 0
description Artificial intelligence (AI) could transform healthcare provision, possibly improving patient safety and clinician decision-making, and mitigating the effects of staff shortages. However, there are concerns - voiced by regulators and policy-makers - over whether AI and clinical decision support systems (CDSSs) are trusted by relevant stakeholders, and more importantly whether such tools are worthy of trust. Yet, the meaning ascribed to trust and trustworthiness is often implicit, and it may be unclear what or who is being trusted. We address these issues, focusing for the most part on the perspective(s) of clinicians. Empirical studies suggest clinicians’ concerns about the use of AI/CDSSs include the accuracy of advice given and potential legal liability if a patient is harmed. Onora O’Neill’s conceptualisation of trust and trustworthiness provides the framework for our analysis. Through unpacking and reflecting upon these two concepts we gain greater clarity over the meaning given to them by a range of stakeholders; minimise the extent to/ways in which stakeholders are talking at cross purposes; and maintain the value of trust and trustworthiness as useful concepts in debates around the use of AI and CDSSs.
published_date 2023-11-27T08:21:41Z
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