LungAI

by Oatmeal Health  · Based in United States → — AI-Powered Lung Cancer Screening + Diagnosis
Oncology Pulmonology Radiology
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Regulatory Status Disclosed

Overview

Oatmeal Health’s LungAI is an advanced AI software solution specifically designed to enhance lung cancer screening programs for physicians and healthcare professionals. This platform integrates seamlessly with existing chest CT computer-aided detection (CADe) systems, providing a sophisticated tool for pulmonary nodule risk assessment.

LungAI leverages a multi-faceted approach to generate malignancy risk scores, incorporating:

  • Detailed Imaging Features: Analyzing characteristics of pulmonary nodules from CT scans.
  • Temporal Analysis: Comparing current imaging with prior examinations to track changes over time.
  • Patient Demographics: Integrating relevant patient information to refine risk predictions.

By operating within supported CADe software already familiar to radiologists, LungAI avoids disrupting established workflows. Its primary goal is to assist lung cancer screening programs by:

  • Identifying higher-risk patients more effectively.
  • Standardizing the process of malignancy risk assessment.
  • Facilitating appropriate and timely follow-up pathways for patients.

Beyond risk assessment, Oatmeal Health is also developing operational and navigation workflows to help health systems improve patient follow-up within lung cancer screening programs, ensuring a more comprehensive and structured approach to early detection and management.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

  • AI-based pulmonary nodule malignancy risk assessment
  • Temporal comparison using prior CT examinations
  • Whole-lung image analysis
  • Patient demographic integration
  • Integration with supported CADe platforms
  • Lung cancer screening workflow support
  • Clinical follow-up and navigation infrastructure
  • Designed for radiology and lung screening programs

Use Cases

  • Lung cancer screening programs
  • Pulmonary nodule risk assessment
  • Standardizing risk assessment in radiology
  • Improving patient follow-up for lung cancer screening
  • Supporting radiologists in interpreting chest CTs
  • Enhancing clinical workflow for lung screening

What Physicians Need to Know

DICOM Support & Standards
LungAI reads the full DICOM image, operating on the entire DICOM voxel tensor.
PACS Integration Method
LungAI offers native CADe and PACS integration, delivering a PDF report with the malignancy score directly into the existing PACS workflow. It integrates without new logins, separate viewers, or workflow disruption.
Reading Room Workflow Impact
The tool is designed to augment Lung-RADS, providing a continuous 0-100 malignancy score as an additional data point for adjudicating borderline cases. It aims to reduce cognitive burden and increase confidence for radiologists.
AI Model Architecture (deep learning approach)
LungAI utilizes a 100-million-parameter vision transformer for deep learning analysis, performing full 3D image analysis. It considers nodule morphology, texture, tissue context, nodule type (solid, part-solid, GGO), prior scan changes, and patient demographics (age, sex, pack-years smoked).
Processing Speed (per study)
LungAI has a read time of 1.5 to 3 minutes per study when paired with a CADe platform.
FDA Clearance Pathway (510k/De Novo)
LungAI is a medical device pending FDA 510(k) clearance.
Supported Modalities (CT/MRI/X-ray/US)
LungAI accepts low-dose CT (LDCT) scans as input.
Sensitivity & Specificity Data
In an internal retrospective study on the NLST held-out dataset (pre-FDA clearance), LungAI demonstrated 94% sensitivity and approximately 82% specificity, with an AUROC of 0.96.
RSNA/ACR Validation
The platform's risk stratification capabilities align with established protocols like Lung-RADS.
Physician Tip

LungAI provides a continuous malignancy score (0-100) that can help stratify risk beyond Lung-RADS categories, especially for borderline cases. This can assist in prioritizing high-risk patients for timely follow-up and potentially extending follow-ups for low-risk indeterminate nodules. The automatic registration and comparison with prior scans can save manual review time. Remember that LungAI is a decision support tool and does not replace the reading physician's judgment or make treatment recommendations.

LungAI is designed for native integration with existing CADe and PACS systems, eliminating the need for new workstations, logins, or separate viewers. It is compatible with major CADe software (e.g., Coreline aView LCS) and scanners from GE, Philips, and Siemens. Deployment can be cloud-based (HIPAA-secure Google Cloud) or on-premise via Docker with a REST API endpoint.

Details

Category Radiology & Imaging AI
Pricing Unknown
  • Oatmeal Health states that there are no one-size-fits-all contracts and that they configure engagement models based on goals, timeline, budget, and use case
  • For health systems and FQHCs, their AI is deployed to close internal screening gaps
  • For nonprofits, government agencies, and life sciences partners, they offer access to their FQHC network to reach underserved patients at scale
  • They also mention that their platform improves access, outcomes, and revenue at no cost to partners
Free TrialUnknown
DeploymentCloud-based. Oatmeal Health uses Google Cloud to spin up secure environments, run large-scale model training, and deploy their reimbursable lung cancer diagnostic.
Mobile AppNo explicit mention of a mobile app for patients or providers.
API AvailableUnknown
TrainingUnknown
Target SizeOatmeal Health partners with Community Health Centers (FQHCs), Health/Imaging Systems, and Pharma/Medtech/Biotech companies. They aim to serve large populations and have contracts covering 1.6 million patients nationwide through a joint venture partner.
Compliance
BAA AvailableUnknown AI-estimated
HIPAA CompliantUnknown AI-estimated
FDA Status Pending. lungai is a medical device pending fda 510(k) clearance. oatmeal health anticipates being among the first fda-cleared ai solutions for lung cancer screening. AI-estimated

LungAI is a medical device pending FDA 510(k) clearance and is not yet FDA-cleared or commercially available. All clinical performance data reflects retrospective studies on held-out datasets, and results may differ in prospective clinical use. Oatmeal Health is developing an integrated CADe-CADx product that combines Coreline Soft's AI nodule detection engine (CADe) with Oatmeal Health's malignancy prediction algorithm (CADx). This product aims to quantify lesion risk and provide AI-driven classification criteria to improve early diagnostic accuracy and enhance efficiency in clinical decision-making for the U.S. low-dose chest CT lung cancer screening market. This product will be billable under CMS-issued CPT codes.
Integrations
EHR Not specified
Specialties Oncology, Pulmonology, Radiology

Social Proof

Customersunknown
Notable
Community Health Centers (FQHCs)Health/Imaging SystemsPharma / Medtech / Biotechand Payers. They have partnerships with health systems and imaging centers. They also partner with nonprofits and government agencies.

Support & Reliability

Training ProvidedUnknown

Ratings & Reviews

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Clinical Value
Ease of Use
Integration
Support & Docs
Value for Money

Press & Coverage

The Oatmeal Bite
We're Launching Clinical Validation Studies at...
Oatmeal Health is initiating clinical validation studies for its LungAI CADx platform at three prominent academic medical centers: Mass General Brigham, University of Alabama at Birmingham, and UCSF.
2026-06
The Oatmeal Bite - Substack
AI Rewrites Lung Nodule Triage Rules
This article discusses how AI, specifically Oatmeal Health's LungAI system, is changing lung nodule triage by providing a continuous malignancy score from 0 to 100, moving beyond the traditional Lung-RADS categories.
2026-06
Oatmeal Health
Hospital Price Transparency Enforcement 2026
This article from Oatmeal Health's blog discusses hospital price transparency enforcement in 2026, with LungAI mentioned as a medical device pending FDA 510(k) clearance.
2026-06
Oatmeal Health
Section 330 Cuts Could Shutter 1,300 FQHCs
This article highlights the potential impact of proposed budget cuts to the HRSA Community Health Center Fund, which could lead to the closure of many Federally Qualified Health Centers (FQHCs), and mentions LungAI's pending FDA clearance.
2026-06
Oatmeal Health
FDA Deploys Agentic AI Across Agency
This article reports on the FDA's unprecedented deployment of agentic AI across the agency, impacting regulatory strategies for medical device companies like Oatmeal Health, whose LungAI is pending 510(k) clearance.
2025-12
Oatmeal Health
AI Predicts Breast Cancer Risk from Mammograms
This article discusses the FDA authorization of Clairity Breast, an AI system that predicts five-year breast cancer risk from mammograms, and mentions LungAI's pending FDA 510(k) clearance.
2025-12
Oatmeal Health
FDA TEMPO Pilot Transforms Digital Health
The FDA's TEMPO pilot program, launched in January 2026, allows digital health companies to bypass certain premarket approvals, with Oatmeal Health's LungAI noted as a medical device pending FDA 510(k) clearance.
2026-01
Oatmeal Health
In the Press - Oatmeal Health
This page aggregates various press, news, and media mentions of Oatmeal Health, including announcements of their AI diagnostic platform launch and partnerships to expand cancer screening services.
2026-04

Videos

Product demos, reviews, and walkthroughs for LungAI.

View all on YouTube

Frequently Asked Questions

LungAI is designed for seamless integration with most modern PACS (Picture Archiving and Communication Systems) and EMR (Electronic Medical Record) systems through standard protocols like DICOM and HL7. Typical IT requirements involve secure network connectivity, adequate server capacity (either on-premise or cloud-based depending on the deployment model), and collaboration with your IT department for initial setup and ongoing maintenance.
LungAI holds regulatory clearances from key bodies such as the FDA (Food and Drug Administration) in the United States and carries the CE mark for conformity in the European Union. We are fully compliant with HIPAA (Health Insurance Portability and Accountability Act) and GDPR (General Data Protection Regulation) to ensure patient data privacy and security.
LungAI's performance metrics, including high sensitivity, specificity, and area under the curve (AUC) values, are rigorously validated across various lung pathologies such as nodules, pneumonia, and emphysema. These metrics are established through extensive studies on diverse imaging modalities like X-ray and CT scans, with detailed reports available upon request.
LungAI distinguishes itself through its advanced deep learning algorithms, which offer superior accuracy and a broader spectrum of detectable pathologies compared to many competitors. Our solution also provides customizable workflows and comprehensive reporting features, designed to integrate more intuitively into a radiologist's existing workflow.
LungAI offers flexible pricing models, including subscription-based options that can be tailored to the volume of studies or the specific features required by your practice. We provide different tiers to accommodate various needs, from smaller clinics to large hospital networks, and our sales team can provide a detailed quote based on your specific operational requirements.
While highly accurate, LungAI, like all AI systems, has limitations; for instance, very rare pathologies or extremely subtle findings might require additional human review. The system is designed to flag ambiguous cases or images with significant artifacts for the radiologist's attention, ensuring that clinical decisions are always made with human oversight.
Upon implementation, we provide comprehensive training for your radiology team and IT staff to ensure a smooth transition and optimal utilization of LungAI. We offer continuous technical support, regular software updates to enhance performance and introduce new features, and dedicated account management to address any ongoing needs or questions.

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Suggest an Edit → | Last Verified: 2026-07-03 | First Added: 2026-07-03
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