ReviewEuropean radiology experimental2018
Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine.
Review in European radiology experimental, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 290 papers, 13 of them syntheses that pooled 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.
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
290 citing papers in PubMed, 13 syntheses or guidelines pooled it, 799 citations in OpenAlex.
- Diagnostic accuracy of AI-assisted versus independent physician interpretation for bone fractures: a systematic review and meta-analysis.European radiology experimental · 2026Pooled it
- Artificial intelligence for risk-stratified breast cancer screening: a systematic review of evidence, clinical integration, and ethical implications in risk assessment tools.La Radiologia medica · 2026Pooled it
- AI-Based Models for Risk Prediction in MASLD: A Systematic Review.Digestive diseases and sciences · 2026Pooled it
- Deep learning-based automated segmentation of intracerebral haemorrhage, intraventricular haemorrhage and perihaematomal oedema on non-contrast CT.European stroke journal · 2026Pooled it
- The value of artificial intelligence in ultrasound imaging for predicting molecular subtypes of breast cancer: a meta-analysis.Frontiers in oncology · 2026Pooled it
- Diagnostic accuracy of artificial intelligence for obstructive sleep apnea detection: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Facilitators and Barriers to Implementing AI in Routine Medical Imaging: Systematic Review and Qualitative Analysis.Journal of medical Internet research · 2025Pooled it
- Prediction models for the complication incidence and survival rate of dental implants-a systematic review and critical appraisal.International journal of implant dentistry · 2025Pooled it
- Capturing artificial intelligence applications' value proposition in healthcare - a qualitative research study.BMC health services research · 2024Pooled it
- Systematic review of artificial intelligence development and evaluation for MRI diagnosis of knee ligament or meniscus tears.Skeletal radiology · 2024Pooled it
- Diagnostic Performance of Artificial Intelligence-Centred Systems in the Diagnosis and Postoperative Surveillance of Upper Gastrointestinal Malignancies Using Computed Tomography Imaging: A Systematic Review and Meta-Analysis of Diagnostic Accuracy.Annals of surgical oncology · 2022Pooled it
- Ethics of AI in Radiology: A Review of Ethical and Societal Implications.Frontiers in big data · 2022Pooled it
- Diagnostic accuracy of artificial intelligence for detecting gastrointestinal luminal pathologies: A systematic review and meta-analysis.Frontiers in medicine · 2022Pooled it
- Should AI results be disclosed in mammography reports? A randomised survey study of patient responses to concordant and discordant interpretations.European radiology · 2026Trial
- Application of machine learning model in predicting the likelihood of blood transfusion after hip fracture surgery.Aging clinical and experimental research · 2023Trial
- Predicting BRAFV600E mutations in papillary thyroid carcinoma using six machine learning algorithms based on ultrasound elastography.Scientific reports · 2023Trial
- Advancements in echocardiography for congenital heart defects (CHDs): a literature review.Annals of medicine and surgery (2012) · 2026Review
- Radiomic Analysis of Mandibular Bone Changes in Patients with Chronic Myeloid Leukemia on Panoramic Radiographs.Journal of imaging informatics in medicine · 2026Article
- Cross-cultural adaptation and psychometric evaluation of the Turkish ATRAI-14 among radiology residents: a multicenter national survey.BMC medical education · 2026Article
- Bronchial Wall T2-Weighted MRI Signal: An Emerging Investigational Non-Ionizing Imaging Biomarker in Severe Asthma.Biomedicines · 2026Article
230 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
One of the most promising areas of health innovation is the application of artificial intelligence (AI), primarily in medical imaging. This article provides basic definitions of terms such as "machine/deep learning" and analyses the integration of AI into radiology. Publications on AI have drastically increased from about 100-150 per year in 2007-2008 to 700-800 per year in 2016-2017. Magnetic resonance imaging and computed tomography collectively account for more than 50% of current articles. Neuroradiology appears in about one-third of the papers, followed by musculoskeletal, cardiovascular, breast, urogenital, lung/thorax, and abdomen, each representing 6-9% of articles. With an irreversible increase in the amount of data and the possibility to use AI to identify findings either detectable or not by the human eye, radiology is now moving from a subjective perceptual skill to a more objective science. Radiologists, who were on the forefront of the digital era in medicine, can guide the introduction of AI into healthcare. Yet, they will not be replaced because radiology includes communication of diagnosis, consideration of patient's values and preferences, medical judgment, quality assurance, education, policy-making, and interventional procedures. The higher efficiency provided by AI will allow radiologists to perform more value-added tasks, becoming more visible to patients and playing a vital role in multidisciplinary clinical teams.
Indexed as
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.