Evidence map›Paper›PMID 34480239›Full record

ArticleScience and engineering ethics2021

Implementing Ethics in Healthcare AI-Based Applications: A Scoping Review.

Magali Goirand, Elizabeth Austin, Robyn Clay-Williams

Abstract readScoping Review
PubMed Publisher
In one paragraph

Article in Science and engineering ethics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing 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

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

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  6. Scoping review on natural language processing applications in counselling and psychotherapy.British journal of psychology (London, England : 1953) · 2026
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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.

Magali GoirandAustralian Institute of Health Innovation, Macquarie University, Sydney, Australia. magali.goirandampaire@hdr.mq.edu.au.ORCID http://orcid.org/0000-0003-3877-5701
Elizabeth AustinAustralian Institute of Health Innovation, Macquarie University, Sydney, Australia.ORCID http://orcid.org/0000-0002-8438-2362
Robyn Clay-WilliamsAustralian Institute of Health Innovation, Macquarie University, Sydney, Australia.ORCID http://orcid.org/0000-0002-6107-7445

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A number of Artificial Intelligence (AI) ethics frameworks have been published in the last 6 years in response to the growing concerns posed by the adoption of AI in different sectors, including healthcare. While there is a strong culture of medical ethics in healthcare applications, AI-based Healthcare Applications (AIHA) are challenging the existing ethics and regulatory frameworks. This scoping review explores how ethics frameworks have been implemented in AIHA, how these implementations have been evaluated and whether they have been successful. AI specific ethics frameworks in healthcare appear to have a limited adoption and they are mostly used in conjunction with other ethics frameworks. The operationalisation of ethics frameworks is a complex endeavour with challenges at different levels: ethics principles, design, technology, organisational, and regulatory. Strategies identified in this review are proactive, contextual, technological, checklist, organisational and/or evidence-based approaches. While interdisciplinary approaches show promises, how an ethics framework is implemented in an AI-based Healthcare Application is not widely reported, and there is a need for transparency for trustworthy AI.

Indexed as

Artificial IntelligenceDelivery of Health CareEthics, MedicalOrganizationsTechnologyAIBioethicsCare robotsCDSSEthicsHealthcareIATMachine learning

Identifiers

What OpenQuestion holds

Textmetadata
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.