ArticleInsights into imaging2024
Impact of AI on radiology: a EuroAIM/EuSoMII 2024 survey among members of the European Society of Radiology.
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 29 papers, 4 of them syntheses that pooled it.
What it found
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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
29 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Cost-effectiveness of artificial intelligence tools in radiology: a systematic review.European radiology · 2026Pooled it
- CT and MRI radiomics in cardiovascular risk prediction: a systematic review and meta-analysis by the EuSoMII Radiomics Auditing Group.European radiology · 2026Pooled it
- Whole-body MRI for staging and follow-up of primary musculoskeletal tumours: a systematic review.The British journal of radiology · 2026Pooled it
- The impact of sarcopenia on sarcoma patients: a systematic review and meta-analysis.La Radiologia medica · 2025Pooled it
- Current status and future directions of AI in prostate cancer detection on MRI: a special report from the ESUR prostate MRI working group authors.European radiology · 2026Article
- Human-AI interaction and collaboration in radiology: from conceptual frameworks to responsible implementation.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Review
- Use, Concerns, and Perspectives on AI in Health Care Among French Health Professionals and Students: Web-Based Cross-Sectional Survey.JMIR medical education · 2026Article
- Bridging the AI chasm in oncology: a standardized platform to enable the in silico clinical validation of AI models within the CHAIMELEON Project.European radiology experimental · 2026Article
- Shifting from black box decisions to informed decision-making in using artificial intelligence to analyze prostate MRI.Abdominal radiology (New York) · 2026Review
- Barriers, Facilitators, and Intention to Use AI for Breast Cancer Diagnosis: Mixed Methods Study Among Austrian Physicians With and Without AI Experience.Journal of medical Internet research · 2026Article
- 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
- How green are large language models for radiology report labelling? Comparing human, rule-based and hybrid workflows.Insights into imaging · 2026Article
- Bridging functional bionanomaterials and the clinic: strategic communication as a translational enabler.Journal of nanobiotechnology · 2026Review
- The real world of Italian new-generation radiologists: challenges and career expectations.La Radiologia medica · 2026Article
- Safe for kids? AI medical devices in radiology overlook paediatric suitability.European radiology · 2026Review
- Vendors' perspectives on AI implementation in medical imaging and oncology: a cross-sectional survey.European radiology · 2026Article
- Can AI write reports like a radiologist? A blinded evaluation of large language model-generated lumbar spine MRI reports.European radiology experimental · 2026Observational
- MICAFlow: Fast and robust MRI preprocessing bridging research neuroimaging and clinical practice.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Mind the gap: challenges and future directions for content-based image retrieval in clinical radiology.Frontiers in radiology · 2026Article
- From performance to practice: knowledge-distilled segmentator for on-premises clinical workflows.npj health systems · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In order to assess the perceptions and expectations of the radiology staff about artificial intelligence (AI), we conducted an online survey among ESR members (January-March 2024). It was designed considering that conducted in 2018, updated according to recent advancements and emerging topics, consisting of seven questions regarding demographics and professional background and 28 AI questions. Of 28,000 members contacted, 572 (2%) completed the survey. AI impact was predominantly expected on breast and oncologic imaging, primarily involving CT, mammography, and MRI, and in the detection of abnormalities in asymptomatic subjects. About half of responders did not foresee an impact of AI on job opportunities. For 273/572 respondents (48%), AI-only reports would not be accepted by patients; and 242/572 respondents (42%) think that the use of AI systems will not change the relationship between the radiological team and the patient. According to 255/572 respondents (45%), radiologists will take responsibility for any AI output that may influence clinical decision-making. Of 572 respondents, 274 (48%) are currently using AI, 153 (27%) are not, and 145 (25%) are planning to do so. In conclusion, ESR members declare familiarity with AI technologies, as well as recognition of their potential benefits and challenges. Compared to the 2018 survey, the perception of AI's impact on job opportunities is in general slightly less optimistic (more positive from AI users/researchers), while the radiologist's responsibility for AI outputs is confirmed. The use of large language models is declared not only limited to research, highlighting the need for education in AI and its regulations. CRITICAL RELEVANCE STATEMENT: This study critically evaluates the current impact of AI on radiology, revealing significant usage patterns and clinical implications, thereby guiding future integration strategies to enhance efficiency and patient care in clinical radiology. KEY POINTS: The survey examines ESR member's views about the impact of AI on radiology practice. AI use is relevant in CT and MRI, with varying impacts on job roles. AI tools enhance clinical efficiency but require radiologist oversight for patient acceptance.
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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.