Evidence map›Paper›PMID 41987962›Full record

ArticleAsian bioethics review2026

Attitudes Towards the Use of Artificial Intelligence in Healthcare: A Conjoint Analysis Survey in Singapore.

Hui Jin Toh, Hui Yun Chan, Thomas Ploug, Søren Holm, Tamra Lysaght

Abstract read
In one paragraph

Article in Asian bioethics review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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.

Hui Jin TohCentre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
Hui Yun ChanCentre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.ORCID 0000-0001-6084-0261
Thomas PlougCentre for AI Ethics, Law, and Policy, Aalborg University, Aalborg, Denmark.
Søren HolmCentre for Social Ethics and Policy, Department of Law, School of Social Sciences, University of Manchester, Manchester, UK.
Tamra LysaghtSydney Health Ethics, Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Novel artificial intelligence (AI) is increasingly being used as a clinical decision support tool in healthcare. Despite AI's growing use and improved quality in clinical decision-making, questions persist about potential harms and the lack of transparency in their algorithms. Implementation of AI technologies in healthcare must align with local norms and ethical standards if the purported benefits are to be achieved in specific contexts. Using choice-based conjoint analysis, we examined how Singaporeans evaluate different principles related to AI decision-making in healthcare. Six attributes were included: decision type, severity, explainability, quality, responsibility, and discrimination. Among 596 respondents, 51% reported fear that AI would unintentionally harm humans, while 87% feared increased surveillance. Responsibility had the highest relative importance (31.5%) for AI use in healthcare, followed by explainability (27.7%) and discrimination (15.9%). The most valued attribute levels were AI recommendations being as explainable as doctors', doctors retaining responsibility for treatment decisions, and AI systems being tested for discrimination. Our respondents showed higher levels of trust, hope, and fear toward AI, with a stronger preference for explainability over doctor responsibility. While having AI outperform doctors in generating clinical suggestions is desirable, principles such as explainability, human oversight, and fairness are more important for the people whose lives AI will impact.

Indexed as

AI ethicsAI in healthcareArtificial intelligenceAttitudinal researchFairnessMoral responsibilityTransparency

Identifiers

PMID41987962
PMCPMC13076719

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.