ReviewKorean journal of radiology2024
Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals.
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 22 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
22 citing papers in PubMed, 42 citations in OpenAlex.
- Fine-tuned multimodal GPT-4o for generating diagnostic impressions in breast magnetic resonance imaging: insights into non-mass enhancement lesions.Quantitative imaging in medicine and surgery · 2026Article
- A robust vision language model for molecular status prediction and radiology report generation in adult-type diffuse gliomas.NPJ digital medicine · 2026Article
- Assessing the Reliability of Large Language Models in Detecting Acute Knee Fractures on Radiographs:A Comparative Study.Journal of clinical practice and research · 2026Article
- Review
- Applying large language model for automated quality scoring of radiology requisitions using a standardized criteria.European radiology · 2026Article
- Investigation of Pressure Injuries With Visual ChatGPT Integration: A Descriptive Cross-Sectional Study.Journal of advanced nursing · 2026Article
- 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
- Enhancing Radiographic Diagnosis: CycleGAN-Based Methods for Reducing Cast Shadow Artifacts in Wrist Radiographs.Journal of imaging informatics in medicine · 2025Article
- Clinical Applications, Challenges & Pitfalls, and Recommendations for Large Language Model and Generative AI in Musculoskeletal Imaging.Journal of the Korean Society of Radiology · 2025Review
- Phenotype augmentation using generative AI for isocitrate dehydrogenase mutation prediction in glioma.Scientific reports · 2025Article
- Generative Artificial Intelligence in Prostate Cancer Imaging.Balkan medical journal · 2025Review
- Application of Artificial Intelligence in Thoracic Radiology: A Narrative Review.Tuberculosis and respiratory diseases · 2025Article
- Consensus on the Potential of Large Language Models in Healthcare: Insights from a Delphi Survey in Korea.Healthcare informatics research · 2025Article
- Generative AI for Diagnostic Medical Imaging: A Review.Current medical imaging · 2025Review
- Synthetic data in medical imaging within the EHDS: a path forward for ethics, regulation, and standards.Frontiers in digital health · 2025Review
- Image-Based Generative Artificial Intelligence in Radiology: Comprehensive Updates.Korean journal of radiology · 2024Review
- Conceptual review of outcome metrics and measures used in clinical evaluation of artificial intelligence in radiology.La Radiologia medica · 2024Review
- [Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging].Journal of the Korean Society of Radiology · 2024Review
- Comparative Evaluation of the Accuracies of Large Language Models in Answering VI-RADS-Related Questions.Korean journal of radiology · 2024Article
- Accuracy of Large Language Models in Thyroid Nodule-Related Questions Based on the Korean Thyroid Imaging Reporting and Data System (K-TIRADS).Korean journal of radiology · 2024Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
The emergence of Chat Generative Pre-trained Transformer (ChatGPT), a chatbot developed by OpenAI, has garnered interest in the application of generative artificial intelligence (AI) models in the medical field. This review summarizes different generative AI models and their potential applications in the field of medicine and explores the evolving landscape of Generative Adversarial Networks and diffusion models since the introduction of generative AI models. These models have made valuable contributions to the field of radiology. Furthermore, this review also explores the significance of synthetic data in addressing privacy concerns and augmenting data diversity and quality within the medical domain, in addition to emphasizing the role of inversion in the investigation of generative models and outlining an approach to replicate this process. We provide an overview of Large Language Models, such as GPTs and bidirectional encoder representations (BERTs), that focus on prominent representatives and discuss recent initiatives involving language-vision models in radiology, including innovative large language and vision assistant for biomedicine (LLaVa-Med), to illustrate their practical application. This comprehensive review offers insights into the wide-ranging applications of generative AI models in clinical research and emphasizes their transformative potential.
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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.