ReviewKorean journal of radiology2025
Multimodal Large Language Models in Medical Imaging: Current State and Future Directions.
Review in Korean journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large language models in healthcare: applications, evaluation frameworks, and governance pathways - a scoping review and multidimensional framework.Frontiers in digital health · 2026Pooled it
- Multimodal Large Language Models for Dental Chart Image Interpretation: Cross-Sectional Benchmarking Study With Students and Clinicians.JMIR medical education · 2026Article
- Large language models for lumbar spondylolisthesis detection: a multi-center pilot comparative radiographic accuracy study.European journal of orthopaedic surgery & traumatology : orthopedie traumatologie · 2026Article
- Can ChatGPT pass the polish national medical specialization examination in orthopedics and traumatology?Archives of orthopaedic and trauma surgery · 2026Article
- Zero-shot segmentation and tumor diameter measurement of soft tissue sarcomas on MRI using a multimodal large language model.Skeletal radiology · 2026Article
- A CNN-based liver ultrasound plane recognition system with multimodal large language model-generated interpretable feedback.Abdominal radiology (New York) · 2026Article
- Single-session agreement of ChatGPT and Gemini treatment recommendations with multidisciplinary tumor board decisions in thoracic oncology.BMC cancer · 2026Observational
- Risk of Widening Global Inequalities in Artificial Intelligence Adoption in Radiology: A Latin American Perspective.Korean journal of radiology · 2026Article
- Potential and Pitfalls of Multimodal Large Language Models in Cerebral Palsy Hip Surveillance: A Radiographic Interpretation Study Assessing Educational Utility.Journal of clinical medicine · 2026Article
- Diagnostic and Prognostic Non-Invasive Markers in Bladder Cancer.Diagnostics (Basel, Switzerland) · 2026Review
- Zero-shot multimodal large language models underperform a domain-trained CNN baseline in pediatric wrist fracture detection.Scientific reports · 2026Article
- AI assisted photographic posture evaluation: a comparative study of ChatGPT and expert physiotherapists.BMC sports science, medicine & rehabilitation · 2026Article
- Performance, safety, and limitations of multimodal large language models in wound image assessment.Scientific reports · 2026Article
- Complementary roles of convolutional neural networks and multimodal large language models (ChatGPT) in radiographic implant identification : A clinical and exploratory study.Orthopadie (Heidelberg, Germany) · 2026Article
- Benchmarking Multimodal Vision Frontier Models With Lumbar Spine MRIs for Grading Lumbar Spinal Stenosis.Global spine journal · 2026Article
- Artificial Intelligence in Occupational Health Surveillance: Evaluating AI-Assisted ILO Classification of Radiographs of Pneumoconioses.La Medicina del lavoro · 2026Article
- Large language model-assisted radiology reporting in a single-radiologist implementation: a retrospective cohort study interpreted through a UTAUT lens.Abdominal radiology (New York) · 2026Article
- Article
- AI in Hand and Wrist Radiography: Multimodal Large Language Models for Distal Radius Fracture Detection and Characterization.Diagnostics (Basel, Switzerland) · 2026Article
- A robust vision language model for molecular status prediction and radiology report generation in adult-type diffuse gliomas.NPJ digital medicine · 2026Article
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15 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Multimodal large language models (MLLMs) are emerging as powerful tools in medicine, particularly in radiology, with the potential to serve as trusted artificial intelligence (AI) partners for clinicians. In radiology, these models integrate large language models (LLMs) with diverse multimodal data sources by combining clinical information and text with radiologic images of various modalities, ranging from 2D chest X-rays to 3D CT/MRI. Methods for achieving this multimodal integration are rapidly evolving, and the high performance of freely available LLMs may further accelerate MLLM development. Current applications of MLLMs now span automatic generation of preliminary radiology report, visual question answering, and interactive diagnostic support. Despite these promising capabilities, several significant challenges hinder widespread clinical adoption. MLLMs require access to large-scale, high-quality multimodal datasets, which are scarce in the medical domain. Risks of hallucinated findings, lack of transparency in decision-making processes, and high computational demands further complicate implementation. This review summarizes the current capabilities and limitations of MLLMs in medicine-particularly in radiology-and outlines key directions for future research. Critical areas include incorporating region-grounded reasoning to link model outputs to specific image regions, developing robust foundation models pre-trained on large-scale medical datasets, and establishing strategies for the safe and effective integration of MLLMs into clinical practice.
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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.