Evidence map›Paper›PMID 37953871›Full record

ReviewBJR open2023

Responsible AI practice and AI education are central to AI implementation: a rapid review for all medical imaging professionals in Europe.

Gemma Walsh, Nikolaos Stogiannos, Riaan van de Venter, Clare Rainey, Winnie Tam, Sonyia McFadden, Jonathan P McNulty, Nejc Mekis, Sarah Lewis, Tracy O'Regan and 9 more

Abstract readReview
In one paragraph

Review in BJR open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

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

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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

19 authors.

Gemma WalshDivision of Midwifery & Radiography, City University of London, London, United Kingdom.
Nikolaos Stogiannos
Riaan van de Venter
Clare RaineySchool of Health Sciences, Ulster University, Derry~Londonderry, Northern Ireland.
Winnie TamDivision of Midwifery & Radiography, City University of London, London, United Kingdom.
Sonyia McFaddenSchool of Health Sciences, Ulster University, Coleraine, United Kingdom.
Jonathan P McNultyUniversity College Dublin, School of Medicine, Dublin, Ireland.
Nejc MekisMedical Imaging and Radiotherapy Department, University of Ljubljana, Faculty of Health Sciences, Ljubljana, Slovenia.
Sarah LewisDiscipline of Medical Imaging Science, Sydney School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, Australia.
Tracy O'ReganThe Society and College of Radiographers, London, United Kingdom.
Amrita KumarFrimley Health NHS Foundation Trust, Frimley, United Kingdom.
Merel HuismanDepartment of Radiology, University Medical Center Utrecht, Utrecht, Netherlands.
Sotirios Bisdas
Elmar Kotter
Daniel Pinto Dos Santos
Cláudia Sá Dos ReisSchool of Health Sciences (HESAV), University of Applied Sciences and Arts Western Switzerland (HES-SO), Lausanne, Switzerland.
Peter van Ooijen
Adrian P Brady
Christina Malamateniou

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) has transitioned from the lab to the bedside, and it is increasingly being used in healthcare. Radiology and Radiography are on the frontline of AI implementation, because of the use of big data for medical imaging and diagnosis for different patient groups. Safe and effective AI implementation requires that responsible and ethical practices are upheld by all key stakeholders, that there is harmonious collaboration between different professional groups, and customised educational provisions for all involved. This paper outlines key principles of ethical and responsible AI, highlights recent educational initiatives for clinical practitioners and discusses the synergies between all medical imaging professionals as they prepare for the digital future in Europe. Responsible and ethical AI is vital to enhance a culture of safety and trust for healthcare professionals and patients alike. Educational and training provisions for medical imaging professionals on AI is central to the understanding of basic AI principles and applications and there are many offerings currently in Europe. Education can facilitate the transparency of AI tools, but more formalised, university-led training is needed to ensure the academic scrutiny, appropriate pedagogy, multidisciplinarity and customisation to the learners' unique needs are being adhered to. As radiographers and radiologists work together and with other professionals to understand and harness the benefits of AI in medical imaging, it becomes clear that they are faced with the same challenges and that they have the same needs. The digital future belongs to multidisciplinary teams that work seamlessly together, learn together, manage risk collectively and collaborate for the benefit of the patients they serve.

Identifiers

PMID37953871
PMCPMC10636340

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.