Why M4CXR?
Easy Draft Report Creation
Automatically generates draft CXR reports.
Improved Accuracy
Delivers consistent and reliable draft reports through an AI model trained on extensive CXR datasets.
Reduced Workload for Clinicians
Minimizes repetitive tasks, allowing clinicians to focus more on patient care.
CXR AI Report
Generate CXR report drafts instantly with M4CXR' generative AI - to enhance workflow efficiency, reduce workload, and improve diagnostic accuracy.
Key Features
Comprehensive CXR Report Drafts
Powered by AI trained to detect abnormalities across all major anatomical regions in chest X-rays—including the lungs, heart, mediastinum, bones, and upper abdomen—providing thorough and consistent findings without missing key pathologies.
Nodule/Mass
Consolidation
Fibrosis
Atelectasis
Emphysema
Tuberculosis
Pleural Effusion
Pneumothorax
Cardiomegaly
Hilar Enlargement
Rib Fracture
Pneumoperitoneum
41+
Three Customizable CXR Report Draft Styles
M4CXR adapts to individual preferences, allowing customization of reporting styles and formats to deliver a seamless and intuitive user experience.
Standardized
Findings:
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Bilateral pleural effusion.
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Pulmonary edema with haziness in both lungs.
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Tracheostomy state.
Impression:
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R/O Combined pneumonia.
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Rec) Chest CT for further evaluation of pulmonary edema and pleural effusion.
Verbose
Findings:
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Bilateral pleural effusion.
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Pulmonary edema with haziness in both lungs.
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Tracheostomy state.
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No Cardiomegaly.
Impression:
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R/O Combined pneumonia.
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Rec) Chest CT for further evaluation of pulmonary edema and pleural effusion.
Abbreviated
Findings:
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Bilat pl. effusion.
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Pulm. edema w/ haziness in both lungs.
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Tracheostomy state.
Impression:
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R/O combined PNA.
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Rec) Chest CT for further evaluation of pulm. edema and pl. effusion.
Simultaneous PA & Lateral Processing
M4CXR processes chest X-ray images in both PA and lateral views simultaneously, enhancing diagnostic accuracy through multi-view analysis.
Publications
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Ro Woon Lee, et al., “Comparative Analysis of M4CXR, an LLM-Based Chest X-Ray Report Generation Model, and ChatGPT in Radiological Interpretation”, Journal of Clinical Medicine, 2024.
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Jonggwon Park, et al., “M4CXR Exploring Multi-task Potentials of Multi-modal Large Language Models for Chest X-ray Interpretation”, IEEE Transactions on Neural Networks and Learning Systems, 2025.
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Jonggwon Park, et al., “RadZero: Similarity-Based Cross-Attention for Explainable Vision-Language Alignment in Radiology with Zero-Shot Multi-Task Capability”, NeurIPS 2025.
Conference Abstracts
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Quantitative Comparison and Reader-Based Evaluation of a Vision-Language Model for Chest Radiograph Interpretation, KCR 2025
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Evaluating a Vision-Language Artificial Intelligence Model as a Screening Tool for Preoperative Chest Radiographs, KCR 2025
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Diagnostic Comparison Between a Vision-Language AI Model and General Practitioners for Detecting Pneumoperitoneum on Chest Radiographs, KCR 2025
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Semantic-Enriched Multi-Task Learning Enhances Chest Radiograph Analysis in Both AP and PA Views, KCR 2025
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Quantitative and Qualitative Assessment of an AI Model for Chest Radiograph Report Generation, KCR 2025
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Exploring Vision-Language Ai-Assisted Double Reading for Chest Radiograph Reports via Synthetic Errors (Oral), KCR 2025
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Diagnostic Performance of a Large Language Model in Multimodal Retrieval-Augmented Radiology Report Generation_A Comparative Study with an Experienced Human Reader, ESNR 2025
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Honggeun Jo, at al., Performance Comparison of a Report Generating AI Model and a Segmentation Based AI, ESNR 2025
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Multimodal Retrieval-Augmented Radiology Reporting_Diagnostic Comparisons of Single- and Multi-View LLMs, ESNR 2025
This solution is currently under development and has not yet been cleared or approved by regulatory authorities.The information provided is based on internal research and is intended for reference only.Specifications and capabilities may change as the product undergoes further validation and regulatory review.
