ArticleDiagnostics (Basel, Switzerland)2023
Validation of a Deep Learning Chest X-ray Interpretation Model: Integrating Large-Scale AI and Large Language Models for Comparative Analysis with ChatGPT.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnosis accuracy of ultrasonography for acute colonic diverticulitis: a diagnostic meta-analysis.Abdominal radiology (New York) · 2026Pooled it
- Performance evaluation of domain-specific and general-purpose AI models for chest radiograph interpretation: a comparative study.BMC medical imaging · 2026Article
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- Trends and Trajectories in the Rise of Large Language Models in Radiology: Scoping Review.JMIR medical informatics · 2025Article
- Artificial Intelligence-Driven Drafting of Chest X-Ray Reports: 2025 Position Statement From the Korean Society of Thoracic Radiology Based on an Expert Survey.Korean journal of radiology · 2025Article
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- Developing artificial intelligence tools for institutional review board pre-review: A pilot study on ChatGPT's accuracy and reproducibility.PLOS digital health · 2025Article
- Comparative Evaluation of Large Language and Multimodal Models in Detecting Spinal Stabilization Systems on X-Ray Images.Journal of clinical medicine · 2025Article
- Artificial intelligence in fracture detection on radiographs: a literature review.Japanese journal of radiology · 2025Review
- Integrating AI and Assistive Technologies in Healthcare: Insights from a Narrative Review of Reviews.Healthcare (Basel, Switzerland) · 2025Review
- Decoding wisdom: Evaluating ChatGPT's accuracy and reproducibility in analyzing orthopantomographic images for third molar assessment.Computational and structural biotechnology journal · 2025Article
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- Advancements in Artificial Intelligence for Medical Computer-Aided Diagnosis.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial Intelligence in Medical Imaging: Analyzing the Performance of ChatGPT and Microsoft Bing in Scoliosis Detection and Cobb Angle Assessment.Diagnostics (Basel, Switzerland) · 2024Article
- ChatGPT's Accuracy on Magnetic Resonance Imaging Basics: Characteristics and Limitations Depending on the Question Type.Diagnostics (Basel, Switzerland) · 2024Article
- Clinical applications of generative artificial intelligence in radiology: image translation, synthesis, and text generation.BJR artificial intelligence · 2024Review
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3 authors.
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Abstract
This study evaluates the diagnostic accuracy and clinical utility of two artificial intelligence (AI) techniques: Kakao Brain Artificial Neural Network for Chest X-ray Reading (KARA-CXR), an assistive technology developed using large-scale AI and large language models (LLMs), and ChatGPT, a well-known LLM. The study was conducted to validate the performance of the two technologies in chest X-ray reading and explore their potential applications in the medical imaging diagnosis domain. The study methodology consisted of randomly selecting 2000 chest X-ray images from a single institution's patient database, and two radiologists evaluated the readings provided by KARA-CXR and ChatGPT. The study used five qualitative factors to evaluate the readings generated by each model: accuracy, false findings, location inaccuracies, count inaccuracies, and hallucinations. Statistical analysis showed that KARA-CXR achieved significantly higher diagnostic accuracy compared to ChatGPT. In the 'Acceptable' accuracy category, KARA-CXR was rated at 70.50% and 68.00% by two observers, while ChatGPT achieved 40.50% and 47.00%. Interobserver agreement was moderate for both systems, with KARA at 0.74 and GPT4 at 0.73. For 'False Findings', KARA-CXR scored 68.00% and 68.50%, while ChatGPT scored 37.00% for both observers, with high interobserver agreements of 0.96 for KARA and 0.97 for GPT4. In 'Location Inaccuracy' and 'Hallucinations', KARA-CXR outperformed ChatGPT with significant margins. KARA-CXR demonstrated a non-hallucination rate of 75%, which is significantly higher than ChatGPT's 38%. The interobserver agreement was high for KARA (0.91) and moderate to high for GPT4 (0.85) in the hallucination category. In conclusion, this study demonstrates the potential of AI and large-scale language models in medical imaging and diagnostics. It also shows that in the chest X-ray domain, KARA-CXR has relatively higher accuracy than ChatGPT.
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