ArticlePediatric radiology2024
Capability of multimodal large language models to interpret pediatric radiological images.
Article in Pediatric radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- 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
- Comparative performance of recent and prior large language models and pediatric residents on pediatric in-training examination questions.Scientific reports · 2026Article
- Article
- Diagnostic Accuracy and Stability of Multimodal Large Language Models for Hand Fracture Detection: A Multi-Run Evaluation on Plain Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- ChatGPT-4V prompt: A tool to enhance junior radiologists' diagnostic capabilities in cystic renal masses to senior-level accuracy.Computational and structural biotechnology journal · 2026Article
- Trends and Trajectories in the Rise of Large Language Models in Radiology: Scoping Review.JMIR medical informatics · 2025Article
- Intra- and Inter-Observer Reliability of ChatGPT-4o in Thyroid Nodule Ultrasound Feature Analysis Based on ACR TI-RADS: An Image-Based Study.Diagnostics (Basel, Switzerland) · 2025Article
- Accuracy of Large Language Models in Detecting Cases Requiring Immediate Reporting in Pediatric Radiology: A Feasibility Study Using Publicly Available Clinical Vignettes.Korean journal of radiology · 2025Article
- Large Language Models in Medical Diagnostics: Scoping Review With Bibliometric Analysis.Journal of medical Internet research · 2025Article
- Assessing the feasibility of ChatGPT-4o and Claude 3-Opus in thyroid nodule classification based on ultrasound images.Endocrine · 2025Article
- A systematic review and meta-analysis of GPT-based differential diagnostic accuracy in radiological cases: 2023-2025.Frontiers in radiology · 2025Article
- Article
- Diagnostic Performance of Multimodal Large Language Models for Grading Pediatric Vesicoureteral Reflux on MCUG: A Preliminary Study.Journal of Indian Association of Pediatric SurgeonsArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere is a dearth of artificial intelligence (AI) development and research dedicated to pediatric radiology. The newest iterations of large language models (LLMs) like ChatGPT can process image and video input in addition to text. They are thus theoretically capable of providing impressions of input radiological images.
objectiveTo assess the ability of multimodal LLMs to interpret pediatric radiological images. MATERIALS AND
methodsThirty medically significant cases were collected and submitted to GPT-4 (OpenAI, San Francisco, CA), Gemini 1.5 Pro (Google, Mountain View, CA), and Claude 3 Opus (Anthropic, San Francisco, CA) with a short history for a total of 90 images. AI responses were recorded and independently assessed for accuracy by a resident and attending physician. 95% confidence intervals were determined using the adjusted Wald method.
resultsOverall, the models correctly diagnosed 27.8% (25/90) of images (95% CI=19.5-37.8%), were partially correct for 13.3% (12/90) of images (95% CI=2.7-26.4%), and were incorrect for 58.9% (53/90) of images (95% CI=48.6-68.5%).
conclusionMultimodal LLMs are not yet capable of interpreting pediatric radiological images.
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