Evidence map›Paper›PMID 38793025›Full record

ArticleJournal of personalized medicine2024

Clearing the Fog: A Scoping Literature Review on the Ethical Issues Surrounding Artificial Intelligence-Based Medical Devices.

Alessia Maccaro, Katy Stokes, Laura Statham, Lucas He, Arthur Williams, Leandro Pecchia, Davide Piaggio

Abstract readScoping Review
In one paragraph

Article in Journal of personalized medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 2 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

15 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  9. [Research on interdisciplinary issues of artificial intelligence medical devices].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025
    Review
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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

7 authors.

Alessia MaccaroApplied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK.ORCID 0000-0001-9338-9884
Katy StokesApplied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK.ORCID 0000-0003-0766-6836
Laura StathamApplied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK.ORCID 0009-0006-6576-795X
Lucas HeApplied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK.
Arthur WilliamsApplied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK.
Leandro PecchiaApplied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK.
Davide PiaggioApplied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK.ORCID 0000-0001-5408-9360

Funding

MRC Doctoral Training Partnership MR/N014294/1UK Research and Innovation 10031483
6 · The paper itself

Abstract

The use of AI in healthcare has sparked much debate among philosophers, ethicists, regulators and policymakers who raised concerns about the implications of such technologies. The presented scoping review captures the progression of the ethical and legal debate and the proposed ethical frameworks available concerning the use of AI-based medical technologies, capturing key themes across a wide range of medical contexts. The ethical dimensions are synthesised in order to produce a coherent ethical framework for AI-based medical technologies, highlighting how transparency, accountability, confidentiality, autonomy, trust and fairness are the top six recurrent ethical issues. The literature also highlighted how it is essential to increase ethical awareness through interdisciplinary research, such that researchers, AI developers and regulators have the necessary education/competence or networks and tools to ensure proper consideration of ethical matters in the conception and design of new AI technologies and their norms. Interdisciplinarity throughout research, regulation and implementation will help ensure AI-based medical devices are ethical, clinically effective and safe. Achieving these goals will facilitate successful translation of AI into healthcare systems, which currently is lagging behind other sectors, to ensure timely achievement of health benefits to patients and the public.

Indexed as

artificial intelligenceethicshealthcaremachine learningmedical devicesregulatory affairs

Identifiers

PMID38793025
PMCPMC11121798

What Socratic 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 Socratic graph.