Evidence map›Paper›PMID 31682262›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2020

A governance model for the application of AI in health care.

Sandeep Reddy, Sonia Allan, Simon Coghlan, Paul Cooper

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 252 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
252citing papers in PubMed, 8 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

252 citing papers in PubMed, 8 syntheses or guidelines pooled it.

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  11. AI In Leukemia Diagnostics: Complementing the Pathologist's Role.International journal of laboratory hematology · 2026
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192 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

4 authors.

Sandeep ReddySchool of Medicine, Geelong, Deakin University, Australia.
Sonia AllanDeakin Law School, Melbourne, Deakin University, Australia.
Simon CoghlanSchool of Computing and Information Systems, University of Melbourne, Melbourne, Australia.
Paul CooperSchool of Medicine, Geelong, Deakin University, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As the efficacy of artificial intelligence (AI) in improving aspects of healthcare delivery is increasingly becoming evident, it becomes likely that AI will be incorporated in routine clinical care in the near future. This promise has led to growing focus and investment in AI medical applications both from governmental organizations and technological companies. However, concern has been expressed about the ethical and regulatory aspects of the application of AI in health care. These concerns include the possibility of biases, lack of transparency with certain AI algorithms, privacy concerns with the data used for training AI models, and safety and liability issues with AI application in clinical environments. While there has been extensive discussion about the ethics of AI in health care, there has been little dialogue or recommendations as to how to practically address these concerns in health care. In this article, we propose a governance model that aims to not only address the ethical and regulatory issues that arise out of the application of AI in health care, but also stimulate further discussion about governance of AI in health care.

Indexed as

Delivery of Health CareArtificial IntelligenceEthics, MedicalGovernment RegulationHumansModels, TheoreticalOrganizational PolicyWorkflowartificial intelligenceethicsgovernance frameworkhealthcareregulation

Identifiers

PMID31682262
PMCPMC7647243

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.