ReviewPhysics in medicine and biology2024
Advancing medical imaging with language models: featuring a spotlight on ChatGPT.
Review in Physics in medicine and biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed.
- Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications, challenges, and future directions.Medical physics · 2026Review
- Symptom-Only Localization of Brainstem Ischemia Using Large Language Models Versus Neurologists in Diffusion-Weighted Imaging-Positive Cases: Retrospective Single-Center Study.JMIR formative research · 2026Article
- Prompt mechanisms in medical imaging: A comprehensive survey.Innovation (Cambridge (Mass.)) · 2026Review
- Intelligent Head and Neck CTA Report Quality Detection with Large Language Models.Journal of imaging informatics in medicine · 2026Article
- Could a New Method of Acromiohumeral Distance Measurement Emerge? Artificial Intelligence vs. Physician.Journal of imaging informatics in medicine · 2026Article
- AgentMRI: A Vison Language Model-Powered AI System for Self-regulating MRI Reconstruction with Multiple Degradations.Journal of imaging informatics in medicine · 2026Article
- Evaluation of the Diagnostic Performance of ChatGPT in Radiographic Staging of Sacroiliitis According to the Modified New York Criteria.Archives of rheumatology · 2026Article
- Diagnostic accuracy and repeatability of ChatGPT using textual and radiographic data in reversible pulpitis: a retrospective diagnostic study.Frontiers in bioinformatics · 2026Article
- Deep learning-driven image captioning: Progress through transformers and large language models.PloS one · 2026Article
- Benchmarking Vision LLMs in fetal ultrasound interpretation: a five-point expert evaluation of standard vs. custom prompts.Biomedical engineering online · 2025Article
- A Review of the Opportunities and Challenges with Large Language Models in Radiology: The Road Ahead.AJNR. American journal of neuroradiology · 2025Review
- Enhancing the Accuracy of Human Phenotype Ontology Identification: Comparative Evaluation of Multimodal Large Language Models.Journal of medical Internet research · 2025Article
- The Role of Large Language Models (LLMs) in Hepato-Pancreato-Biliary Surgery: Opportunities and Challenges.Cureus · 2025Article
- Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration.ArXiv · 2025Article
- Artificial intelligence for diagnosing bladder pathophysiology: An updated review and future prospects.Bladder (San Francisco, Calif.) · 2025Review
- Fast MRI Reconstruction Using Deep Learning-based Compressed Sensing: A Systematic Review.ArXiv · 2024Article
- Identification of middle cerebral artery stenosis in transcranial Doppler using a modified VGG-16.Frontiers in neurology · 2024Article
- ChatGPT in Radiology: The Advantages and Limitations of Artificial Intelligence for Medical Imaging Diagnosis.Cureus · 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
6 authors.
Funding
Abstract
This review paper aims to serve as a comprehensive guide and instructional resource for researchers seeking to effectively implement language models in medical imaging research. First, we presented the fundamental principles and evolution of language models, dedicating particular attention to large language models. We then reviewed the current literature on how language models are being used to improve medical imaging, emphasizing a range of applications such as image captioning, report generation, report classification, findings extraction, visual question response systems, interpretable diagnosis and so on. Notably, the capabilities of ChatGPT were spotlighted for researchers to explore its further applications. Furthermore, we covered the advantageous impacts of accurate and efficient language models in medical imaging analysis, such as the enhancement of clinical workflow efficiency, reduction of diagnostic errors, and assistance of clinicians in providing timely and accurate diagnoses. Overall, our goal is to have better integration of language models with medical imaging, thereby inspiring new ideas and innovations. It is our aspiration that this review can serve as a useful resource for researchers in this field, stimulating continued investigative and innovative pursuits of the application of language models in medical imaging.
Indexed as
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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.