ReviewNature reviews. Cancer2018
Artificial intelligence in radiology.
Review in Nature reviews. Cancer, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 4 registered trials, which are not on this map. Cited by 1,591 papers, 18 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.
Deep Learning Super Resolution Reconstruction for Fast and Motion Robust T2-weighted Prostate MRI
Novel Neuroradiological Workflow for the Assisted DIAgnosis and Management of DEMentia with Artificial Intelligence
AI Reliance in Diagnostic Radiology Among Intern Doctors in Palestine: A Triple-Arm, Triple-Blind, Parallel-Design Randomized Controlled Trial
Evaluation of the Effectiveness of Artificial Intelligence in Determining the Accurate Working Length of Curved Root Canals in Endodontic Treatment
Who cites it
1,591 citing papers in PubMed, 18 syntheses or guidelines pooled it.
- Stakeholder Perspectives on the Integration of AI in Diabetes Care: Systematic Review of Qualitative Studies.Journal of medical Internet research · 2026Pooled it
- Artificial Intelligence for Diagnosis, Risk Stratification, and Prognosis of Neuroblastoma - A Systematic Review and Meta-Analysis.Current oncology reports · 2026Pooled it
- Artificial Intelligence (AI)-based tools in the diagnosis and management of prostate cancer: a systematic review and meta-analysis.Prostate cancer and prostatic diseases · 2026Pooled it
- Artificial intelligence models in cerebral infarction: performance and applications in diagnosis, classification, grading, treatment, and disease course prediction-a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Artificial intelligence in the diagnostic imaging of developmental dysplasia of the hip: a systematic review.EFORT open reviews · 2026Pooled it
- Computed tomography-based artificial intelligence for predicting preoperative microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis.La Radiologia medica · 2026Pooled it
- Shifting the paradigm in intracranial aneurysm detection with deep learning: A diagnostic accuracy meta-analysis and meta-regression.Neurosurgical review · 2026Pooled it
- "First, do no harm" in the digital era: examining the practicality of the European Health Data Space proposal and ethical implications of artificial intelligence: A systematic literature review.BMC medical ethics · 2026Pooled it
- Use of artificial intelligence in education and training of radiology.Frontiers in radiology · 2026Pooled it
- Deep learning models for pancreatic cancer detection on CT: a meta-analysis.Frontiers in medicine · 2026Pooled it
- Artificial intelligence for opportunistic screening of osteoporosis across multiple imaging modalities: a systematic review.Frontiers in medicine · 2026Pooled it
- Application and integration of deep learning in tumour radiomics: bibliometrics and visualisation analysis.Frontiers in artificial intelligence · 2026Pooled it
- Machine learning-based methods in diagnosing cardiac amyloidosis: a meta-analysis.Frontiers in cardiovascular medicine · 2026Pooled it
- Effectiveness and implementation challenges of mobile low-dose computed tomography units for early lung cancer detection: a systematic review.Frontiers in public health · 2026Pooled it
- Diagnostic accuracy of artificial intelligence for tuberculosis detection from cough sounds: a systematic review and meta-analysis.Frontiers in artificial intelligence · 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
- Use of large language models for providing automated feedback in medical imaging education: a systematic review.Frontiers in medicine · 2026Pooled it
- Artificial intelligence for medical imaging education: bibliometric and visual analysis.Frontiers in medicine · 2026Pooled it
- Deep learning algorithm for automatic detection of acute ischemic stroke on noncontrast brain CT.Scientific reports · 2026Trial
- Diagnostic performance of an open-source artificial intelligence model for detecting chest infections on after-hours emergency department chest radiographs in Singapore.BMJ health & care informatics · 2026Article
1,531 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
5 authors.
Funding
Abstract
Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.
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.