Evidence map›Paper›PMID 41189744›Full record

ArticleAI & society2026

Is

Jemima Winifred Allen, Ivar Rodríguez Hannikainen, Julian Savulescu, Dominic Wilkinson, Brian David Earp

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in AI & society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Review
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

5 authors.

Jemima Winifred AllenMonash University, Melbourne, Australia.
Ivar Rodríguez HannikainenUniversity of Granada, Granada, Spain.
Julian SavulescuUniversity of Oxford, Oxford, United Kingdom.
Dominic WilkinsonMonash University, Melbourne, Australia.
Brian David EarpNational University of Singapore, Singapore, Singapore.

Funding

Wellcome Trust 203132
6 · The paper itself

Abstract

Healthcare systems often delegate surgical consent-seeking to members of the treating team other than the surgeon (e.g., junior doctors in the UK and Australia). Yet, little is known about public attitudes toward this practice compared to emerging AI-supported options. This first large-scale empirical study examines how laypeople evaluate the validity and liability risks of using an AI-supported surgical consent system (

Indexed as

Artificial intelligence in healthcareConsent delegationEmpirical bioethicsInformed consentLarge language modelsMedical ethics

Identifiers

PMID41189744
PMCPMC7618318

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.