Evidence map›Paper›PMID 37208863›Full record

ReviewJournal of the American Medical Informatics Association : JAMIA2023

Implementation frameworks for end-to-end clinical AI: derivation of the SALIENT framework.

Anton H van der Vegt, Ian A Scott, Krishna Dermawan, Rudolf J Schnetler, Vikrant R Kalke, Paul J Lane

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
56citing 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

56 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.

Anton H van der VegtCentre for Health Services Research, The University of Queensland, Brisbane, Australia.ORCID 0000-0001-5642-5188
Ian A ScottDepartment of Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital, Brisbane, Australia.
Krishna DermawanCentre for Information Resilience, The University of Queensland, St Lucia, Australia.
Rudolf J SchnetlerSchool of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, Australia.
Vikrant R KalkePatient Safety and Quality, Clinical Excellence Queensland, Queensland Health, Brisbane, Australia.
Paul J LaneSafety Quality & Innovation, The Prince Charles Hospital, Queensland Health, Brisbane, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo derive a comprehensive implementation framework for clinical AI models within hospitals informed by existing AI frameworks and integrated with reporting standards for clinical AI research. MATERIALS AND

methods(1) Derive a provisional implementation framework based on the taxonomy of Stead et al and integrated with current reporting standards for AI research: TRIPOD, DECIDE-AI, CONSORT-AI. (2) Undertake a scoping review of published clinical AI implementation frameworks and identify key themes and stages. (3) Perform a gap analysis and refine the framework by incorporating missing items.

resultsThe provisional AI implementation framework, called SALIENT, was mapped to 5 stages common to both the taxonomy and the reporting standards. A scoping review retrieved 20 studies and 247 themes, stages, and subelements were identified. A gap analysis identified 5 new cross-stage themes and 16 new tasks. The final framework comprised 5 stages, 7 elements, and 4 components, including the AI system, data pipeline, human-computer interface, and clinical workflow. DISCUSSION: This pragmatic framework resolves gaps in existing stage- and theme-based clinical AI implementation guidance by comprehensively addressing the what (components), when (stages), and how (tasks) of AI implementation, as well as the who (organization) and why (policy domains). By integrating research reporting standards into SALIENT, the framework is grounded in rigorous evaluation methodologies. The framework requires validation as being applicable to real-world studies of deployed AI models.

conclusionsA novel end-to-end framework has been developed for implementing AI within hospital clinical practice that builds on previous AI implementation frameworks and research reporting standards.

Indexed as

HospitalsUser-Computer InterfaceHumansWorkflowAI frameworkAI implementationartificial intelligencehealthcare frameworkmachine learning

Identifiers

PMID37208863
PMCPMC10436156

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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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.