ArticleEuropean radiology2025
Assessing GPT-4 multimodal performance in radiological image analysis.
Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
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Who cites it
56 citing papers in PubMed.
- [Digitalization in trauma networks : Teleconsultation, real-time situation mapping and artificial intelligence-assisted triage].Unfallchirurgie (Heidelberg, Germany) · 2026Review
- Diagnostic accuracy and consistency of ChatGPT-4o in radiology: influence of image, clinical data, and answer options on performance.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Special article-EchoPeer: a standardized framework for assessing echocardiography reports in the era of artificial intelligence: a recommendation from the Korean Society of Echocardiography AI and Future Strategy Committee.Journal of cardiovascular imaging · 2026Review
- Benchmarking Multimodal Large Language Models for Cardiopulmonary Findings on Chest Radiographs: Sex-Stratified Discrimination and Operating Characteristics.Diagnostics (Basel, Switzerland) · 2026Article
- 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
- Leveraging Large Language Models to Integrate Clinical Knowledge and Machine Learning Predictions for Lymph Node Metastasis Prediction: Development of a Knowledge-Augmented Framework.JMIR medical informatics · 2026Article
- Performance, safety, and limitations of multimodal large language models in wound image assessment.Scientific reports · 2026Article
- Zero-shot performance of a general-purpose vision-language model for pediatric appendicitis diagnosis.Pediatric radiology · 2026Article
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- Intelligent Head and Neck CTA Report Quality Detection with Large Language Models.Journal of imaging informatics in medicine · 2026Article
- Performance of GPT-5 vs GPT-4V for radiological image analysis.European radiology · 2026Article
- Can ChatGPT-5 estimate dental age? A comparative study with Demirjian and Willems methods in paediatric dentistry.BMC oral health · 2026Article
- Pilot Study Comparing Emergency Physician and Artificial Intelligence-supported Interpretations of Electrocardiograms.The western journal of emergency medicine · 2026Article
- Do General-Purpose Multimodal Large Language Models Really See Radiologic Images or Rely on Text?Korean journal of radiology · 2026Article
- Opportunities and Challenges of Visual Large Language Models in Imaging Diagnostics: Lessons from Brain Metastasis Detection in Clinical MRI.Diagnostics (Basel, Switzerland) · 2026Article
- Evaluating the Accuracy and Diagnostic Reasoning of Multimodal Large Language Models in Interpreting Neuroradiology Cases FromKorean journal of radiology · 2026Article
- Implementing generative artificial intelligence in precision oncology: safety, governance, and significance.Journal of hematology & oncology · 2026Review
- Atlas-Assisted Bone Age Estimation from Hand-Wrist Radiographs Using Multimodal Large Language Models: A Comparative Study.Diagnostics (Basel, Switzerland) · 2026Article
- Evaluation of large language models for diagnostic impression generation from brain MRI report findings: a multicenter benchmark and reader study.NPJ digital medicine · 2026Article
- Development and Validation of a Generative Artificial Intelligence-Based Pipeline for Automated Clinical Data Extraction From Electronic Health Records: Technical Implementation Study.JMIR bioinformatics and biotechnology · 2026Article
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7 authors.
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Abstract
objectivesThis study aims to assess the performance of a multimodal artificial intelligence (AI) model capable of analyzing both images and textual data (GPT-4V), in interpreting radiological images. It focuses on a range of modalities, anatomical regions, and pathologies to explore the potential of zero-shot generative AI in enhancing diagnostic processes in radiology.
methodsWe analyzed 230 anonymized emergency room diagnostic images, consecutively collected over 1 week, using GPT-4V. Modalities included ultrasound (US), computerized tomography (CT), and X-ray images. The interpretations provided by GPT-4V were then compared with those of senior radiologists. This comparison aimed to evaluate the accuracy of GPT-4V in recognizing the imaging modality, anatomical region, and pathology present in the images.
resultsGPT-4V identified the imaging modality correctly in 100% of cases (221/221), the anatomical region in 87.1% (189/217), and the pathology in 35.2% (76/216). However, the model's performance varied significantly across different modalities, with anatomical region identification accuracy ranging from 60.9% (39/64) in US images to 97% (98/101) and 100% (52/52) in CT and X-ray images (p < 0.001). Similarly, pathology identification ranged from 9.1% (6/66) in US images to 36.4% (36/99) in CT and 66.7% (34/51) in X-ray images (p < 0.001). These variations indicate inconsistencies in GPT-4V's ability to interpret radiological images accurately.
conclusionWhile the integration of AI in radiology, exemplified by multimodal GPT-4, offers promising avenues for diagnostic enhancement, the current capabilities of GPT-4V are not yet reliable for interpreting radiological images. This study underscores the necessity for ongoing development to achieve dependable performance in radiology diagnostics. CLINICAL RELEVANCE STATEMENT: Although GPT-4V shows promise in radiological image interpretation, its high diagnostic hallucination rate (> 40%) indicates it cannot be trusted for clinical use as a standalone tool. Improvements are necessary to enhance its reliability and ensure patient safety. KEY POINTS: GPT-4V's capability in analyzing images offers new clinical possibilities in radiology. GPT-4V excels in identifying imaging modalities but demonstrates inconsistent anatomy and pathology detection. Ongoing AI advancements are necessary to enhance diagnostic reliability in radiological applications.
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