Evidence map›Paper›PMID 31677530›Full record

ArticleBreast (Edinburgh, Scotland)2020

The ethical, legal and social implications of using artificial intelligence systems in breast cancer care.

Stacy M Carter, Wendy Rogers, Khin Than Win, Helen Frazer, Bernadette Richards, Nehmat Houssami

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Breast (Edinburgh, Scotland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
105citing 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

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

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  20. Artificial intelligence in the diagnosis and management of gynecologic cancer.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2025
    Review

45 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

6 authors.

Stacy M CarterAustralian Centre for Health Engagement, Evidence and Values (ACHEEV), School of Health and Society, Faculty of Social Science, University of Wollongong, Northfields Avenue, New South Wales, 2522, Australia. Electronic address: stacyc@uow.edu.au.
Wendy RogersDepartment of Philosophy and Department of Clinical Medicine, Macquarie University, Balaclava Road, North Ryde, New South Wales, 2109, Australia. Electronic address: wendy.rogers@mq.edu.au.
Khin Than WinCentre for Persuasive Technology and Society, School of Computing and Information Technology, Faculty of Engineering and Information Technology, University of Wollongong, Northfields Avenue, New South Wales, 2522, Australia. Electronic address: win@uow.edu.au.
Helen FrazerScreening and Assessment Service, St Vincent's BreastScreen, 1st Floor Healy Wing, 41 Victoria Parade, Fitzroy, Victoria, 3065, Australia. Electronic address: Helen.Frazer@svha.org.au.
Bernadette RichardsAdelaide Law School, Faculty of the Professions, University of Adelaide, Adelaide, South Australia, 5005, Australia. Electronic address: bernadette.richards@adelaide.edu.au.
Nehmat HoussamiSydney School of Public Health, Faculty of Medicine and Health, Fisher Road, The University of Sydney, New South Wales, 2006, Australia. Electronic address: nehmat.houssami@sydney.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer care is a leading area for development of artificial intelligence (AI), with applications including screening and diagnosis, risk calculation, prognostication and clinical decision-support, management planning, and precision medicine. We review the ethical, legal and social implications of these developments. We consider the values encoded in algorithms, the need to evaluate outcomes, and issues of bias and transferability, data ownership, confidentiality and consent, and legal, moral and professional responsibility. We consider potential effects for patients, including on trust in healthcare, and provide some social science explanations for the apparent rush to implement AI solutions. We conclude by anticipating future directions for AI in breast cancer care. Stakeholders in healthcare AI should acknowledge that their enterprise is an ethical, legal and social challenge, not just a technical challenge. Taking these challenges seriously will require broad engagement, imposition of conditions on implementation, and pre-emptive systems of oversight to ensure that development does not run ahead of evaluation and deliberation. Once artificial intelligence becomes institutionalised, it may be difficult to reverse: a proactive role for government, regulators and professional groups will help ensure introduction in robust research contexts, and the development of a sound evidence base regarding real-world effectiveness. Detailed public discussion is required to consider what kind of AI is acceptable rather than simply accepting what is offered, thus optimising outcomes for health systems, professionals, society and those receiving care.

Indexed as

Breast NeoplasmsTechnology Assessment, BiomedicalArtificial IntelligenceAustraliaDecision Support Systems, ClinicalEarly Detection of CancerFemaleHumansPrecision MedicinePrognosisRisk AssessmentAI (Artificial Intelligence)Breast carcinomaEthical IssuesSocial valuesTechnology Assessment, Biomedical

Identifiers

PMID31677530
PMCPMC7375671

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.