ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2021
Automatic Assignment of Radiology Examination Protocols Using Pre-trained Language Models with Knowledge Distillation.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Personalized CT Protocol Recommendation via Large Language Model: Enabling Fully Automated CT Scanning Workflows.Journal of imaging informatics in medicine · 2026Article
- Automated Prediction of Radiological Protocols Using Retrieval Augmented Generation.Journal of imaging informatics in medicine · 2026Article
- Efficacy of Fine-Tuned Large Language Model in CT Protocol Assignment as Clinical Decision-Supporting System.Journal of imaging informatics in medicine · 2025Article
- Abdominal and Pelvic MRI Protocol Prediction Using Natural Language Processing.Journal of imaging informatics in medicine · 2025Article
- Expanding Horizons: The Realities of CAD, the Promise of Artificial Intelligence, and Machine Learning's Role in Breast Imaging beyond Screening Mammography.Diagnostics (Basel, Switzerland) · 2023Review
- Upstream Machine Learning in Radiology.Radiologic clinics of North America · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Selecting radiology examination protocol is a repetitive, and time-consuming process. In this paper, we present a deep learning approach to automatically assign protocols to computed tomography examinations, by pre-training a domain-specific BERT model (BERT
Indexed as
Identifiers
35308920PMC8861685What 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.