Evidence map›Paper›PMID 32284648›Full record

ArticleBulletin of the World Health Organization2020

Artificial intelligence in health care: accountability and safety.

Ibrahim Habli, Tom Lawton, Zoe Porter

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bulletin of the World Health Organization, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 118 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
118citing papers in PubMed, 6 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

118 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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58 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Ibrahim HabliDepartment of Computer Science, University of York, Deramore Lane, Heslington, York YO10 5GH, England.
Tom LawtonBradford Teaching Hospitals NHS Foundation Trust, Bradford, England.
Zoe PorterDepartment of Philosophy, University of York, York, England.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The prospect of patient harm caused by the decisions made by an artificial intelligence-based clinical tool is something to which current practices of accountability and safety worldwide have not yet adjusted. We focus on two aspects of clinical artificial intelligence used for decision-making: moral accountability for harm to patients; and safety assurance to protect patients against such harm. Artificial intelligence-based tools are challenging the standard clinical practices of assigning blame and assuring safety. Human clinicians and safety engineers have weaker control over the decisions reached by artificial intelligence systems and less knowledge and understanding of precisely how the artificial intelligence systems reach their decisions. We illustrate this analysis by applying it to an example of an artificial intelligence-based system developed for use in the treatment of sepsis. The paper ends with practical suggestions for ways forward to mitigate these concerns. We argue for a need to include artificial intelligence developers and systems safety engineers in our assessments of moral accountability for patient harm. Meanwhile, none of the actors in the model robustly fulfil the traditional conditions of moral accountability for the decisions of an artificial intelligence system. We should therefore update our conceptions of moral accountability in this context. We also need to move from a static to a dynamic model of assurance, accepting that considerations of safety are not fully resolvable during the design of the artificial intelligence system before the system has been deployed.

Indexed as

Artificial IntelligenceDelivery of Health CareSafety ManagementSocial ResponsibilityHealth Facilities

Identifiers

PMID32284648
PMCPMC7133468

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.