All Articles
Intelligence That Sees More: How We Built an AI Model for Automatic Intrapulmonary Lymph Node Classification
Learn how Hiveomics developed an AI model achieving AUC ~0.95 for classifying intrapulmonary lymph nodes, reducing false positives and diagnostic uncertainty in chest CT interpretation.
How AI Helps Us See More: Results of Evaluating the Hiveomics Malignancy Index Among Radiology Residents
A new study shows how the Hiveomics Malignancy Index improves diagnostic accuracy by 8.8% and reduces overdiagnosis of malignant nodules by 7.1 percentage points among radiology residents.
Intelligence That Sees Beyond: The New Hiveomics System for Lung Nodule Analysis
Hiveomics Ltd. is pleased to announce the development of an artificial intelligence system dedicated to classifying pulmonary nodules as pulmonary hamartomas.
How AI-Powered Triage is Transforming Thoracic Radiology Workflows
Discover how autonomous pre-triage systems are processing 100% of chest CT studies, reducing radiologist workload while maintaining diagnostic accuracy and improving patient outcomes.
Deep Learning Architectures for Medical Imaging: From U-Net to Transformers
A technical overview of modern deep learning architectures used in medical imaging, including U-Net variants, attention mechanisms, and vision transformers.
Streamlining Admission Readiness: Automated Document Validation in Radiology
Learn how automated referral validation and document summarization can reduce front-desk processing time by 30-50% while improving admission readiness and reducing scheduling errors.
Machine Learning Approaches to Pulmonary Nodule Detection: A 2025 Update
An overview of the latest deep learning architectures for automated lung nodule detection, including performance metrics, validation strategies, and real-world deployment considerations.
PACS Integration Strategies for AI Radiology Systems: Best Practices
Explore proven integration patterns for connecting AI systems with PACS, RIS, and EHR, including DICOM routing, structured reporting, and workflow orchestration.
Building Effective Concordance Registries: Tracking AI-Human Agreement in Radiology
Learn how concordance registries that track agreement between AI recommendations and physician decisions enable continuous quality improvement, algorithm refinement, and regulatory compliance.
HIPAA Compliance in AI-Powered Radiology Systems: A Practical Guide
Navigate the complexities of HIPAA compliance for AI radiology systems, including de-identification strategies, audit trail requirements, and business associate agreements.