Hwang SI, et al. Korean J Radiol. 2023 Oct;24(10):952-959. https://doi.org/10.3348/kjr.2023.0773

A domain-specific multimodal generative AI model demonstrated potential for high diagnostic accuracy and clinical value in providing preliminary interpretations of chest radiographs for radiologists.

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.

Background/Objectives: This study investigated the diagnostic capabilities of two AI-based tools, M4CXR (research-only version) and ChatGPT-4o, in chest X-ray interpretation. M4CXR is a specialized cloud-based system using advanced large language models (LLMs) for generating comprehensive radiology reports, while ChatGPT, built on the GPT-4 architecture, offers potential in settings with limited radiological expertise. Methods: This study evaluated 826 anonymized chest X-ray images from Inha University Hospital. Two experienced radiologists independently assessed the performance of M4CXR and ChatGPT across multiple diagnostic parameters. The evaluation focused on diagnostic accuracy, false findings, location accuracy, count accuracy, and the presence of hallucinations. Interobserver agreement was quantified using Cohen’s kappa coefficient. Results: M4CXR consistently demonstrated superior performance compared to ChatGPT across all evaluation metrics. For diagnostic accuracy, M4CXR achieved approximately 60–62% acceptability ratings compared to ChatGPT’s 42–45%. Both systems showed high interobserver agreement rates, with M4CXR generally displaying stronger consistency. Notably, M4CXR showed better performance in anatomical localization (76–77.5% accuracy) compared to ChatGPT (36–36.5%) and demonstrated fewer instances of hallucination. Conclusions: The findings highlight the complementary potential of these AI technologies in medical diagnostics. While M4CXR shows stronger performance in specialized radiological analysis, the integration of both systems could potentially optimize diagnostic workflows. This study emphasizes the role of AI in augmenting human expertise rather than replacing it, suggesting that a combined approach leveraging both AI capabilities and clinical judgment could enhance patient care outcomes.

Background: Knee osteoarthritis (OA) is a prevalent degenerative joint disease significantly impacting global health. Early and accurate diagnosis is crucial for effective management, but traditional methods often rely on subjective assessments. This study evaluates the efficacy of a deep learning model implemented through a no-code AI platform for diagnosing and grading knee OA from plain radiographs. Methods: We utilized the Osteoarthritis Initiative (OAI) dataset, comprising knee X-ray data from 1526 patients. The data were split into training (47.0%), validation (26.5%), and test (26.5%) sets. We employed a ResNet101 model on the DEEP:PHI no-code AI platform for image analysis. The model was trained to classify knee OA into five grades (0–4) based on the Kellgren–Lawrence scale. Results: Our AI model demonstrated high accuracy in distinguishing between different OA grades, with particular strength in early-stage detection. The model achieved optimal performance at 20 epochs, suggesting efficient learning dynamics. Grad-CAM visualizations were used to enhance the interpretability of the model’s decision-making process. Conclusions: This study demonstrates the potential of AI, implemented through a no-code platform, to accurately diagnose and grade knee OA from radiographs. The use of a no-code AI platform such as DEEP:PHI represents a step towards democratizing AI in healthcare, enabling the rapid development and deployment of sophisticated medical AI applications without extensive coding expertise. This approach could significantly enhance the early detection and management of knee OA, potentially improving patient outcomes and streamlining clinical workflows.

Purpose: Sacroiliitis refers to the inflammatory condition of the sacroiliac joints, frequently causing lower back pain. It is often associated with systemic conditions. However, its signs on radiographic images can be subtle, which may result in it being overlooked or underdiagnosed. This study aims to utilize artificial intelligence (AI) to create a diagnostic tool for more accurate sacroiliitis detection in radiological images, with the goal of optimizing treatment plans and improving patient outcomes. Materials and Method: The study included 492 patients who visited our hospital. Right sacroiliac joint films were independently evaluated by two musculoskeletal radiologists using the Modified New York criteria (Normal, Grades 1–4). A consensus reading resolved disagreements. The images were preprocessed with Z-score standardization and histogram equalization. The DenseNet121 algorithm, a convolutional neural network with 201 layers, was used for learning and classification. All steps were performed on the DEEP:PHI platform. Result: The AI model exhibited high accuracy across different grades: 94.53% (Grade 1), 95.83% (Grade 2), 98.44% (Grade 3), 96.88% (Grade 4), and 96.09% (Normal cases). Sensitivity peaked at Grade 3 and Normal cases (100%), while Grade 4 achieved perfect specificity (100%). PPVs ranged from 82.61% (Grade 1) to 100% (Grade 4), and NPVs peaked at 100% for Grade 3 and Normal cases. The F1 scores ranged from 64.41% (Grade 1) to 95.38% (Grade 3). Conclusions: The AI diagnostic model showcased a robust performance in detecting and grading sacroiliitis, reflecting its potential to enhance diagnostic accuracy in clinical settings. By facilitating earlier and more accurate diagnoses, this model could substantially impact treatment strategies and patient outcomes.