InferRead Lung CT.AI
Overview
Infervision Medical Technology Co. is a leading AI medical technology company established in 2016, dedicated to enhancing multi-disciplinary healthcare services and operations through artificial intelligence. InferRead Lung CT.AI is a flagship product that leverages state-of-the-art artificial intelligence and deep learning technology to assist radiologists in the detection and analysis of pulmonary nodules during the review of chest computed tomography (CT) scans. Its primary goal is to improve diagnostic accuracy and efficiency in disease screening and diagnosis, particularly for lung cancer. The system provides automated detection, quantification, and classification of lung nodules, including assessing their growth and determining the degree of malignancy. It also identifies incidental findings and supports 3D image reconstruction for surgical planning. InferRead Lung CT.AI has been shown in clinical trials to reduce missed nodules by 35% for radiologists and save healthcare professionals 30% of the time spent reading exams.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- Automated lung segmentation
- Accurate identification and labeling of various nodule types (solid, semi-solid, calcified, ground glass)
- Detection of nodules as small as 4 mm
- Quantitative analysis of nodule characteristics (size, density, volume, malignancy rate)
- Multi-timepoint analysis for tracking nodule changes over time
- Automated lung density analysis for identifying abnormalities like emphysema and ILD
- 3D image reconstruction and visualization for surgical planning
- Preliminary report generation with diagnostic and intervention data
- Integration with PACS and RIS
- Accessible browser-based viewer
Use Cases
- Early detection and diagnosis of lung cancer
- Lung cancer screening programs
- Assisting radiologists in pulmonary nodule detection during CT scan review
- Evaluating nodule growth and determining malignancy
- Identifying incidental findings in chest CT scans
- Supporting longitudinal evaluation and differentiating benign from malignant lesions
What Physicians Need to Know
InferRead Lung CT.AI acts as a powerful 'spell-checker' for lung nodules, significantly enhancing detection accuracy and reducing reading times, which is crucial for high-volume lung cancer screening programs. Leverage its quantitative analysis features for comprehensive lung health assessment and intelligent follow-up to track nodule evolution. The structured reports and multi-parameter outputs provide consistent, quantifiable data to support confident clinical decision-making. While the AI improves sensitivity, always integrate its findings with your expert clinical judgment and the original CT scans. Consider its ability to detect small nodules (as small as 4mm) and identify incidental findings to broaden your diagnostic scope.
InferRead Lung CT.AI offers versatile integration, supporting both on-premise and cloud deployments and is compatible with existing PACS/RIS environments. Its ability to output in DICOM GSPS, DICOM overlay, and PDF facilitates seamless workflow integration. Strategic partnerships with platforms like Viz.ai and TeraRecon (Eureka Clinical AI) indicate a commitment to interoperability and care coordination, providing a robust ecosystem for AI-driven insights. Institutions should evaluate their current infrastructure and workflow to determine the optimal deployment and integration strategy, leveraging its compatibility with various platforms for a streamlined experience.
Details
| Category | Oncology AI, Radiology & Imaging AI |
| Pricing | Contact for pricing — The company offers the system under a number of pricing options to meet various needs. |
| Deployment | Cloud-based and on-premise deployment available |
| Compliance | |
| BAA Available | Unknown AI-estimated |
| HIPAA Compliant | Unknown AI-estimated |
| FDA Status |
1 AI-estimated InferRead Lung CT.AI received its initial U.S. Food and Drug Administration (FDA) 510(k) clearance on August 4, 2020. On May 30, 2025, Infervision announced FDA 510(k) clearance for multiple enhanced features, including automated detection of nodules as small as 4 mm, expanded structured assessments of clinical parameters, lung density analysis, and automatic comparison with prior studies. The provided K240554 is an FDA 510(k) premarket notification. |
| Integrations | |
| EHR | Not specified |
| Specialties | Oncology, Pulmonology, Radiology |
What the Web Says
InferRead Lung CT.AI by Infervision is an AI-powered solution designed to assist radiologists in detecting and characterizing pulmonary nodules in CT scans, aiming to improve diagnostic accuracy and efficiency in lung cancer screening. It has received FDA clearance and CE mark, and is in use in numerous hospitals globally, processing a significant number of cases daily.
Overall: MixedStrengths
- Improved diagnostic accuracy and increased sensitivity in nodule detection, potentially reducing missed cancers.
- Reduced reading times for radiologists, leading to increased efficiency and potentially alleviating radiologist burnout.
- Provides comprehensive nodule analysis, including size, density, volume, type (solid, sub-solid, ground glass), and malignancy rate.
- Integrates with existing clinical PACS environments and supports both on-premise and cloud-based deployment.
- Generates smart structured reports, populating key findings directly into reporting platforms.
- Aids in quantitative analysis of whole lung health, including emphysema and interstitial lung disease.
Limitations
- Lower specificity compared to unaided reading, which can lead to an increase in false positives and potentially unnecessary follow-up scans or interventions.
- Some AI tools for pulmonary nodule detection have been noted to perform poorly on patients with pre-existing lung disease, calling out atelectasis or other parenchymal abnormalities as nodules.
- Limited clinical evidence for use in people without risk factors or those already symptomatic for lung cancer, with NICE guidance suggesting potential benefit primarily for targeted lung cancer screening.
- Concerns about automation bias, where radiologists might hesitate to contradict AI findings, potentially contributing to elevated false positive rates.
- Variability in performance among different AI software platforms for lung cancer detection.
- Challenges remain in standardizing AI approaches and fully integrating them into clinical practice.
Based on reviews from: Infervision, BioWorld, PR Newswire, PMC, Rayscape AI, Reddit (r/medicine, r/science, r/medicalschool), Trakterm, Alma Medical Imaging, Health AI Register, Imaging Technology News, MDPI, Diagnostic Imaging, AuntMinnie, NICE Guidance
Last updated: 2026-07-21
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Videos
Product demos, reviews, and walkthroughs for InferRead Lung CT.AI.
Chen Kuan, CEO of Infervision, which uses AI to detect Lung Lesions
Neurosurgical TV
