ReviewRadiology2025
Open-Source Large Language Models in Radiology: A Review and Tutorial for Practical Research and Clinical Deployment.
Review in Radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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.
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Who cites it
27 citing papers in PubMed.
- Comparative evaluation of generative AI models for chest radiograph report generation in the emergency department.European radiology · 2026Article
- From zero-shot to fine-tuning: optimize large language models for error detection of ultrasound reports.Insights into imaging · 2026Article
- Open-weight language models for procedural impression generation.European radiology · 2026Article
- Automatic extraction of structured information from brain MRI reports using an open-weight large language model.European radiology · 2026Article
- Reporting efficiency in diagnostic imaging: Can plug-and-play general-purpose large language models outperform conventional speech recognition?European radiology · 2026Article
- Implementation of a Locally Deployed Qwen2.5-7B-Instruct Pipeline for Structured Classification of Elbow MRI Reports.Cureus · 2026Article
- Open-source offline-deployable retrieval-augmented large language model for assisting pancreatic cancer staging.Japanese journal of radiology · 2026Article
- Alignment of Policy, Practice, and Patient Safety for Trustworthy AI in Radiology.Radiology. Artificial intelligence · 2026Article
- Who labels best? Radiologists, rules, or large language models for CT reports on pulmonary embolism.European radiology experimental · 2026Article
- Evaluating Large Language Models for Turkish Emergency CT Impression Drafting: Quality, Critical Omissions, and Readability.Journal of imaging informatics in medicine · 2026Article
- Predicting molecular types of adult-type diffuse gliomas based on MRI reports with large language models.European radiology · 2026Article
- Invited Commentary on Comparative Evaluation of Proprietary and Open-Source Large Language Models for Systematic Multi-source Information Extraction in Interventional Oncology.Cardiovascular and interventional radiology · 2026Article
- A multi-agent large language model framework for intelligent vendor evaluation and risk-aware procurement decisions.Scientific reports · 2026Article
- Artificial intelligence-assisted quality assessment of mid-trimester ultrasound examinations using large vision-language models.BMC pregnancy and childbirth · 2026Article
- Artificial Intelligence in Radiology: Hidden Fragilities and the Path to Resilience.Saudi medical journal · 2026Review
- Radiology Research and Publishing Across 2025-2026: Perspectives FromKorean journal of radiology · 2026Article
- Standardizing Heterogeneous MRI Series Description Metadata Using Large Language Models.Journal of imaging informatics in medicine · 2026Article
- Artificial intelligence in endocrine practice: comparing ChatGPT, Gemini, and Claude for adrenal incidentaloma care.Journal of endocrinological investigation · 2026Article
- Efficacy of a large language model in classifying branch-duct intraductal papillary mucinous neoplasms.Abdominal radiology (New York) · 2026Article
- Distillation of a Locally Deployable 8B Language Model for Lung Histology Abstraction: A 33-Report Proof-of-Concept Study.Journal of multidisciplinary healthcare · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Integrating large language models (LLMs) into health care holds substantial potential to enhance clinical workflows and care delivery. However, LLMs also pose serious risks if integration is not thoughtfully executed, with complex challenges spanning accuracy, accessibility, privacy, and regulation. Proprietary commercial LLMs (eg, GPT-4 [OpenAI], Claude 3 Sonnet and Claude 3 Opus [Anthropic], Gemini [Google]) have received much attention from researchers in the medical domain, including radiology. Interestingly, open-source LLMs (eg, Llama 3 and LLaVA-Med) have received comparatively little attention. Yet, open-source LLMs hold several key advantages over proprietary LLMs for medical institutions, hospitals, and individual researchers. The wider adoption of open-source LLMs has been slower, perhaps in part due to the lack of familiarity, accessible computational infrastructure, and community-built tools to streamline their local implementation and customize them for specific use cases. Thus, this article provides a tutorial for the implementation of open-source LLMs in radiology, including examples of commonly used tools for text generation and techniques for troubleshooting issues with prompt engineering, retrieval-augmented generation, and fine-tuning. Implementation-ready code for each tool is provided at
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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.