ReviewAJNR. American journal of neuroradiology2025
A Review of the Opportunities and Challenges with Large Language Models in Radiology: The Road Ahead.
Review in AJNR. American journal of neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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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.
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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
14 citing papers in PubMed.
- Large language models in spine care and research.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Review
- 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
- Evaluating Generative Artificial Intelligence as an Educational Tool for Radiology Resident Report Drafting.Journal of the American College of Radiology : JACR · 2026Article
- Artificial Intelligence in Occupational Health Surveillance: Evaluating AI-Assisted ILO Classification of Radiographs of Pneumoconioses.La Medicina del lavoro · 2026Article
- Comparison of online radiologists and large language model chatbots in responding to common radiology-related questions in Chinese: a cross-sectional comparative analysis.Quantitative imaging in medicine and surgery · 2026Article
- Large language models and large concept models in radiology: Present challenges, future directions, and critical perspectives.World journal of radiology · 2025Review
- Large Language Models for the National Radiological Technologist Licensure Examination in Japan: Cross-Sectional Comparative Benchmarking and Evaluation of Model-Generated Items Study.JMIR medical education · 2025Article
- Large Language Model Versus Manual Review for Clinical Data Curation in Breast Cancer: Retrospective Comparative Study.JMIR medical informatics · 2025Article
- Validation of a Dermatology-Focused Multimodal Large Language Model in Classification of Pigmented Skin Lesions.Diagnostics (Basel, Switzerland) · 2025Article
- Multimodal Large Language Models in Medical Imaging: Current State and Future Directions.Korean journal of radiology · 2025Review
- Clinical Applications, Challenges & Pitfalls, and Recommendations for Large Language Model and Generative AI in Musculoskeletal Imaging.Journal of the Korean Society of Radiology · 2025Review
- Performance of Large Language Models in Recognizing Brain MRI Sequences: A Comparative Analysis of ChatGPT-4o, Claude 4 Opus, and Gemini 2.5 Pro.Diagnostics (Basel, Switzerland) · 2025Article
- Large Language Models in Healthcare and Medical Applications: A Review.Bioengineering (Basel, Switzerland) · 2025Review
- Comparative Evaluation of Large Language and Multimodal Models in Detecting Spinal Stabilization Systems on X-Ray Images.Journal of clinical medicine · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, generative artificial intelligence (AI), particularly large language models (LLMs) and their multimodal counterparts, multimodal large language models, including vision language models, have generated considerable interest in the global AI discourse. LLMs, or pre-trained language models (such as ChatGPT, Med-PaLM, LLaMA), are neural network architectures trained on extensive text data, excelling in language comprehension and generation. Multimodal LLMs, a subset of foundation models, are trained on multimodal data sets, integrating text with another modality, such as images, to learn universal representations akin to human cognition better. This versatility enables them to excel in tasks like chatbots, translation, and creative writing while facilitating knowledge sharing through transfer learning, federated learning, and synthetic data creation. Several of these models can have potentially appealing applications in the medical domain, including, but not limited to, enhancing patient care by processing patient data; summarizing reports and relevant literature; providing diagnostic, treatment, and follow-up recommendations; and ancillary tasks like coding and billing. As radiologists enter this promising but uncharted territory, it is imperative for them to be familiar with the basic terminology and processes of LLMs. Herein, we present an overview of the LLMs and their potential applications and challenges in the imaging domain.
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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.