Evidence map›Paper›PMID 37058329›Full record

ArticleJournal of medical Internet research2023

Learning From Experience and Finding the Right Balance in the Governance of Artificial Intelligence and Digital Health Technologies.

Stephen Gilbert, Stuart Anderson, Martin Daumer, Phoebe Li, Tom Melvin, Robin Williams

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 3 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

36 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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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

6 authors.

Stephen GilbertElse Kröner Fresenius Center for Digital Health, Technische Universität Dresden, Dresden, Germany.ORCID 0000-0002-1997-1689
Stuart AndersonSchool of Social and Political Science, University of Edinburgh, Edinburgh, United Kingdom.ORCID 0000-0002-3035-3478
Martin DaumerSchool of Computation, Information and Technology, Technische Universität München, Munich, Germany.ORCID 0000-0001-9615-7632
Phoebe LiSchool of Law, Politics and Sociology, University of Sussex, Brighton, United Kingdom.ORCID 0000-0001-9171-1729
Tom MelvinSchool of Medicine, Trinity College Dublin, Dublin, Ireland.ORCID 0000-0001-6370-4177
Robin WilliamsSchool of Social and Political Science, University of Edinburgh, Edinburgh, United Kingdom.ORCID 0000-0002-9044-4611

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) and machine learning medical tools have the potential to be transformative in care delivery; however, this change will only be realized if accompanied by effective governance that ensures patient safety and public trust. Recent digital health initiatives have called for tighter governance of digital health. A correct balance must be found between ensuring product safety and performance while also enabling the innovation needed to deliver better approaches for patients and affordable efficient health care for society. This requires innovative, fit-for-purpose approaches to regulation. Digital health technologies, particularly AI-based tools, pose specific challenges to the development and implementation of functional regulation. The approaches of regulatory science and "better regulation" have a critical role in developing and evaluating solutions to these problems and ensuring effective implementation. We describe the divergent approaches of the European Union and the United States in the implementation of new regulatory approaches in digital health, and we consider the United Kingdom as a third example, which is in a unique position of developing a new post-Brexit regulatory framework.

Indexed as

Artificial IntelligenceDelivery of Health CareEuropean UnionHumansMachine LearningUnited Kingdomalgorithm change protocolartificial intelligencehealth careimplementationinterventionmachine learningmedical toolpatientperformanceregulationregulatory frameworksafetytechnologytool

Identifiers

PMID37058329
PMCPMC10148205

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.