ReviewKorean journal of radiology2024
Large Language Models: A Guide for Radiologists.
Review in Korean journal of radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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
47 citing papers in PubMed.
- GPT-4.1 and Llama 3.3 70 fail to detect clinically relevant errors in radiology reports in zero-shot evaluation.European radiology · 2026Article
- Locally deployed context-aware chatbot outperforms generic large language models for guideline-concordant pediatric imaging recommendations.Pediatric radiology · 2026Article
- Large Language Models Versus Human Readers in CAD-RADS 2.0 Categorization of Coronary CT Angiography Reports.Journal of imaging informatics in medicine · 2026Article
- Evaluation of Context-Aware Prompting Techniques for Classification of Tumor Response Categories in Radiology Reports Using Large Language Model.Journal of imaging informatics in medicine · 2026Article
- Benchmarking large language models in breast cancer care: agreement with radiology-led multidisciplinary tumor board decisions.BMC medical informatics and decision making · 2026Article
- LLaMA-XR: A Novel Framework for Radiology Report Generation Using LLaMA and QLoRA Fine Tuning.Bioengineering (Basel, Switzerland) · 2026Article
- Initial Insights Into an Institutional Secure Large Language Model for Magnetic Resonance Imaging Examination Requests: Retrospective Study.Journal of medical Internet research · 2026Article
- Operationalizing Large Language Models for Clinical Research Data Extraction: Methods, Quality Control, and Governance.Journal of medical systems · 2026Review
- Retrieval-augmented generation-enhanced large language models for comprehensive CAD-RADS 2.0 categorization from structured coronary CTA reports.Radiologie (Heidelberg, Germany) · 2026Article
- Artificial Intelligence in Radiology: Hidden Fragilities and the Path to Resilience.Saudi medical journal · 2026Review
- Integrating large language models (LLMs) into neuro-oncologic radiology: a retrospective feasibility assessment of GPT-4o for brain tumor diagnosis.Neuroradiology · 2026Article
- Intra-axial primary brain tumor differentiation: comparing large language models on structured MRI reports vs. radiologists on images.European radiology · 2026Article
- High-Performance Prompting for LLM Extraction of Compression Fracture Findings from Radiology Reports.Journal of imaging informatics in medicine · 2026Article
- Cross-Institutional Evaluation of Large Language Models for Radiology Diagnosis Extraction: A Prompt-Engineering Perspective.Journal of imaging informatics in medicine · 2026Article
- Evaluation of large language models in assigning PI-RADS v2.1 categories for prostate MRI reports.BMC urology · 2026Article
- Assessment of large language models in musculoskeletal radiological anatomy: A comparative study with radiologists.Joint diseases and related surgery · 2026Article
- Evaluating large language models for automated TNM staging from PET-CT reports: a multi-cancer comparative study.Frontiers in digital health · 2026Article
- Readability versus accuracy in LLM-transformed radiology reports: stakeholder preferences across reading grade levels.La Radiologia medica · 2025Article
- Article
- Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM): 2025 Updates.Korean journal of radiology · 2025Review
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) have revolutionized the global landscape of technology beyond natural language processing. Owing to their extensive pre-training on vast datasets, contemporary LLMs can handle tasks ranging from general functionalities to domain-specific areas, such as radiology, without additional fine-tuning. General-purpose chatbots based on LLMs can optimize the efficiency of radiologists in terms of their professional work and research endeavors. Importantly, these LLMs are on a trajectory of rapid evolution, wherein challenges such as "hallucination," high training cost, and efficiency issues are addressed, along with the inclusion of multimodal inputs. In this review, we aim to offer conceptual knowledge and actionable guidance to radiologists interested in utilizing LLMs through a succinct overview of the topic and a summary of radiology-specific aspects, from the beginning to potential future directions.
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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.