ReviewBJR open2023
Responsible AI practice and AI education are central to AI implementation: a rapid review for all medical imaging professionals in Europe.
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.
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.
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.
Who cites it
15 citing papers in PubMed.
- Frameworks encompassing intersectional perspective of artificial intelligence in healthcare. Scoping review.Public health in practice (Oxford, England) · 2026Review
- Towards an accessible, centralised, searchable database for AI courses in Europe: the Artificial Intelligence in Medical Imaging and Radiation Oncology Education (AIMIROE) project.European radiology experimental · 2026Article
- Investigating the Potential of Generative AI Clinical Case-Based Simulations on Radiography Education: A Pilot Study.Journal of imaging informatics in medicine · 2026Article
- Ethical Responsibility in Medical AI: A Semi-Systematic Thematic Review and Multilevel Governance Model.Healthcare (Basel, Switzerland) · 2026Review
- AI Revolution in Radiology, Radiation Oncology and Nuclear Medicine: Transforming and Innovating the Radiological Sciences.Journal of medical imaging and radiation oncology · 2025Review
- Knowledge, attitudes, and practices of cardiovascular health care personnel regarding coronary CTA and AI-assisted diagnosis: a cross-sectional study.Journal of global health · 2025Article
- An In-depth overview of artificial intelligence (AI) tool utilization across diverse phases of organ transplantation.Journal of translational medicine · 2025Review
- R-AI-diographers: a European survey on perceived impact of AI on professional identity, careers, and radiographers' roles.Insights into imaging · 2025Article
- The ethical challenges in the integration of artificial intelligence and large language models in medical education: A scoping review.PloS one · 2025Article
- Evaluating artificial intelligence bias in nephrology: the role of diversity, equity, and inclusion in AI-driven decision-making and ethical regulation.Frontiers in artificial intelligence · 2025Article
- R-AI-diographers: investigating the perceived impact of artificial intelligence on radiographers' careers, roles, and professional identity in the UK.Frontiers in digital health · 2025Article
- Understanding AI's Role in Endometriosis Patient Education and Evaluating Its Information and Accuracy: Systematic Review.JMIR AI · 2024Review
- Technology-enabled patient care in medical radiation sciences: the two sides of the coin.Journal of medical radiation sciences · 2024Article
- Artificial intelligence in rheumatology: status quo and quo vadis-results of a national survey among German rheumatologists.Therapeutic advances in musculoskeletal disease · 2024Article
- Adoption, orchestration, and deployment of artificial intelligence within the National Health Service-facilitators and barriers: an expert roundtable discussion.BJR artificial intelligence · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
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
What OpenQuestion holds
Registered trials
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.