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Cited by 245Artificial Intelligence in Healthcare and EducationDigitalization, Law, and RegulationEthics and Social Impacts of AI

The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.

Clinical AI: opacity, accountability, responsibility and liability

Open access
Smith, Helen·2021·AI & SOCIETY
DOI 10.1007/s00146-020-01019-6
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Abstract

The aim of this literature review was to compose a narrative review supported by a systematic approach to critically identify and examine concerns about accountability and the allocation of responsibility and legal liability as applied to the clinician and the technologist as applied the use of opaque AI-powered systems in clinical decision making. This review questions (a) if it is permissible for a clinician to use an opaque AI system (AIS) in clinical decision making and (b) if a patient was harmed as a result of using a clinician using an AIS’s suggestion, how would responsibility and legal liability be allocated? Literature was systematically searched, retrieved, and reviewed from nine databases, which also included items from three clinical professional regulators, as well as relevant grey literature from governmental and non-governmental organisations. This literature was subjected to inclusion/exclusion criteria; those items found relevant to this review underwent data extraction. This review found that there are multiple concerns about opacity, accountability, responsibility and liability when considering the stakeholders of technologists and clinicians in the creation and use of AIS in clinical decision making. Accountability is challenged when the AIS used is opaque, and allocation of responsibility is somewhat unclear. Legal analysis would help stakeholders to understand their obligations and prepare should an undesirable scenario of patient harm eventuate when AIS were used.

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  1. Association for computing machinery (2018) acm code of ethics and professional conduct. https://www.acm.org/code-of-ethics. Accessed Feb 2018

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Metadata

Authors
Smith, H.
Affiliations
University of Bristol (GB)
Corresponding author
Smith, Helen
ORCID
Smith, Helen
Venue
AI & SOCIETY
Publisher
Springer Science and Business Media LLC
Published
2020-07-25
Volume
36
Issue
2
Pages
535-545
ISSN
  • 0951-5666 (ISSN-L)
  • 1435-5655
DOI
10.1007/s00146-020-01019-6
OpenAlex
W3045238219
Cited by
245
References
27
Citations by year
2026: 73 · 2025: 62 · 2024: 53 · 2023: 35 · 2022: 15 · 2021: 6 · 2020: 1
FWCI
4.20
Type
journal article
Language
en
Open access
Licence
Locations
  • AI & Society (publishedVersion, cc-by)
  • Bristol Research (University of Bristol) (submittedVersion, cc-by)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Law › Digitalization, Law, and Regulation
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
Keywords
clinical AI, algorithmic opacity, accountability, responsibility allocation, legal liability, clinical decision-making, patient harm, clinicians, technologists, algorithmic transparency

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