Evidence map›Paper›PMID 38246898›Full record

ArticleInsights into imaging2024

Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA.

Adrian P Brady, Bibb Allen, Jaron Chong, Elmar Kotter, Nina Kottler, John Mongan, Lauren Oakden-Rayner, Daniel Pinto Dos Santos, An Tang, Christoph Wald and 1 more

Abstract read
In one paragraph

Article in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 1 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

40 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Adrian P BradyUniversity College Cork, Cork, Ireland. adrianbrady@me.com.
Bibb AllenDepartment of Radiology, Grandview Medical Center, Birmingham, AL, USA.
Jaron ChongDepartment of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
Elmar KotterDepartment of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Nina KottlerRadiology Partners, El Segundo, CA, USA.
John MonganDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, USA.
Lauren Oakden-RaynerAustralian Institute for Machine Learning, University of Adelaide, Adelaide, Australia.
Daniel Pinto Dos SantosDepartment of Radiology, University Hospital of Cologne, Cologne, Germany.
An TangDepartment of Radiology, Radiation Oncology, and Nuclear Medicine, Université de Montréal, Montréal, Québec, Canada.
Christoph WaldDepartment of Radiology, Lahey Hospital & Medical Center, Burlington, MA, USA.
John SlavotinekSouth Australia Medical Imaging, Flinders Medical Centre Adelaide, Adelaide, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones.This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.Key points • The incorporation of artificial intelligence (AI) in radiological practice demands increased monitoring of its utility and safety.• Cooperation between developers, clinicians, and regulators will allow all involved to address ethical issues and monitor AI performance.• AI can fulfil its promise to advance patient well-being if all steps from development to integration in healthcare are rigorously evaluated.

Indexed as

Artificial IntelligenceAutomationMachine learningRadiology

Identifiers

PMID38246898
PMCPMC10800328

What OpenQuestion holds

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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.