ReviewRadiologic clinics of North America2021
Upstream Machine Learning in Radiology.
Review in Radiologic clinics of North America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Automated Prediction of Radiological Protocols Using Retrieval Augmented Generation.Journal of imaging informatics in medicine · 2026Article
- Abdominal and Pelvic MRI Protocol Prediction Using Natural Language Processing.Journal of imaging informatics in medicine · 2025Article
- AI solutions to the radiology workforce shortage.npj health systems · 2025Review
- Machine learning algorithms for predicting malignancy grades of lung adenocarcinoma and guiding treatments: CT radiomics-based comparisons.Journal of thoracic disease · 2025Article
- Choosing the right artificial intelligence solutions for your radiology department: key factors to considerDiagnostic and interventional radiology (Ankara, Turkey) · 2024Review
- Deep learning for accelerated and robust MRI reconstruction.Magma (New York, N.Y.) · 2024Review
- Elevating healthcare through artificial intelligence: analyzing the abdominal emergencies data set (TR_ABDOMEN_RAD_EMERGENCY) at TEKNOFEST-2022.European radiology · 2024Article
- MRI of acute neck infections: evidence summary and pictorial review.Insights into imaging · 2023Review
- Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available?Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Machine learning (ML) and Artificial intelligence (AI) has the potential to dramatically improve radiology practice at multiple stages of the imaging pipeline. Most of the attention has been garnered by applications focused on improving the end of the pipeline: image interpretation. However, this article reviews how AI/ML can be applied to improve upstream components of the imaging pipeline, including exam modality selection, hardware design, exam protocol selection, data acquisition, image reconstruction, and image processing. A breadth of applications and their potential for impact is shown across multiple imaging modalities, including ultrasound, computed tomography, and MRI.
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.