RevealAI-Lung
Overview
RevealAI-Lung is an AI-powered, computer-aided diagnostic (CADx) software application designed to assist clinicians in the assessment and characterization of incidentally-detected lung nodules on computed tomography (CT) scans. The software analyzes lung CT images and generates a Malignancy Similarity Index (mSI) score, which helps radiologists make more informed follow-up recommendations to assist in cancer diagnosis. The mSI score is intended for the evaluation of incidentally-detected pulmonary nodules with a diameter of 6-15mm in patients aged 18 years or above. RevealAI-Lung compares features from the selected nodule to a reference image database derived from National Lung Cancer Screening Trial (NLST) patient cases. The software is integrated directly into Picture Archiving and Communication Systems (PACS), aiming to improve workflow.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- Malignancy Similarity Index (mSI) score generation
- Single-click operation for mSI computation
- Integration with PACS for streamlined workflow
- Actionable radiomic insights
- Comparison of nodules to a reference image database (NLST data)
- Improved stability across image types
- Transparency of predictions
- Significant cost savings by reducing unnecessary procedures
- Graphs to interpret nodule relation to confirmed malignant/benign nodules
- Generalizability across exam types and patient populations
Use Cases
- Lung nodule risk assessment
- Early lung cancer assessment
- Assisting radiologists in follow-up recommendations for lung nodules
- Reducing delayed cancer diagnoses
- Reducing false positives in lung nodule management
- Improving nodule classification in incidental lung nodules
What Physicians Need to Know
When utilizing RevealAI-Lung, interpret the Malignancy Similarity Index (mSI) as a relative risk score in conjunction with established clinical guidelines and other patient-specific factors. The provided graphs can offer valuable context by showing how the current patient's lung nodules compare to confirmed malignant or benign cases. The software is intended to augment, not replace, a radiologist's expertise in making final patient management decisions. Focus on the efficiency gains from its PACS integration and quick analysis time to streamline your workflow for incidental lung nodule characterization.
RevealAI-Lung's strength lies in its direct integration capabilities with existing PACS, minimizing workflow disruption. It has established partnerships with key players like Riverain for nodule detection, Fujifilm for PACS, and Sirona for a unified radiology platform, indicating a commitment to interoperability within diverse imaging ecosystems. This allows for seamless incorporation of the mSI score into radiology reports and potentially into RIS systems.
Details
| Category | Oncology AI |
| Pricing |
Subscription
|
| Deployment | On-premise server or cloud-based systems securely connected to facility network |
| Compliance | |
| BAA Available | Unknown AI-estimated |
| HIPAA Compliant | Unknown AI-estimated |
| FDA Status |
Cleared AI-estimated RevealAI-Lung received US Food and Drug Administration (FDA) 510(k) clearance on January 30, 2026, for lung nodule risk assessment. It is a Computer-Assisted Diagnostic (CADx) software intended for the characterization of incidentally-detected lung nodules on CT scans. |
| Integrations | |
| EHR | Not specified |
| Specialties | Oncology, Pulmonology, Radiology |
What the Web Says
RevealAI-Lung is an AI-powered software designed to assist radiologists in assessing lung nodules and determining cancer risk. It generates a Malignancy Similarity Index (mSI) score by comparing nodule characteristics to a database of known outcomes. The software aims to improve early cancer detection, reduce false positives, and streamline workflow by integrating directly into Picture Archiving and Communication Systems (PACS).
Overall: PositiveStrengths
- Earlier diagnosis of lung cancer (up to 40% in studies)
- Reduction in false positives (up to 22% in studies)
- Provides a Malignancy Similarity Index (mSI) score to aid risk assessment
- Integrates directly into PACS, improving workflow
- Clinically validated in multi-center studies
- First radiology AI to be reimbursed in Europe (starting in Germany)
Limitations
- The mSI score is a relative risk score, not a percentage cancer risk
- Not indicated for use by pulmonologists; only intended for radiologists
- Not intended for screen-detected nodules
- Learning the SDK for integration might not be a plug-and-play experience for custom setups
Based on reviews from: Healio, contextflow, Health AI Register, G2, Medical Product Outsourcing, Imaging Technology News, Axis Imaging News, accessdata.fda.gov
Last updated: 2026-08-19
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