Lung AI

by Exo  · Based in United States → — Handheld AI Ultrasound for Smarter Cardiac and Lung Scanning.
Cardiology Emergency Medicine Pulmonology

Contact for pricing
Regulatory Status Disclosed

Overview

Lung AI by Exo is an artificial intelligence application designed to assist healthcare providers in interpreting point-of-care ultrasound (POCUS) lung scans. Running directly on the handheld Exo Iris device, the software operates on-device and real-time without requiring an active internet connection.

The tool is built for clinicians across multiple specialties—including emergency medicine, critical care, internal medicine, and pulmonology—operating in hospitals, clinics, and remote or under-resourced care settings. It integrates into the clinical workflow at the patient’s bedside, allowing providers to perform rapid diagnostic assessments of acute pulmonary symptoms such as dyspnea.

Notable Capabilities:

  • Automatically detects and localizes key pulmonary features, including the presence or absence of A-lines and B-lines.
  • Identifies pleural effusion and consolidation or atelectasis directly on-screen.
  • Recognizes anatomical landmarks, including rib shadows and the pleural line, to guide proper probe placement.
  • Provides real-time quality feedback by generating per-frame and per-clip quality scores for each lung scan to ensure reliable imaging.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

  • AI-powered detection of pleural effusion
  • AI-powered detection of consolidation/atelectasis
  • Real-time feedback and image quality monitoring (SweepAI)
  • Automated AI-based indicators for cardiac conditions (e.g., heart failure, hypertrophy)
  • Guided image capture for optimal scans
  • On-device AI processing (operates without internet)
  • Pulsed-wave Doppler capabilities (on Exo Iris)
  • Exo U (AI-powered POCUS education and training)
  • High accuracy and validated performance across diverse patient populations
  • Detection of A-lines and B-lines for pulmonary edema assessment

Use Cases

  • Diagnosing lung diseases like pneumonia and tuberculosis at the bedside
  • Assessing fluid status, including pulmonary edema and IVC collapsibility
  • Detecting cardiac conditions such as heart failure and cardiac toxicity
  • Enhancing diagnostic capabilities in emergency and critical care settings
  • Supporting point-of-care ultrasound (POCUS) education and training
  • Accelerating patient care pathways to treatment or discharge

What Physicians Need to Know

Cardiac CT/MRI Analysis
Yes, AI-CVD extracts opportunistic screening information from cardiac and lung CT scans. It performs automated analysis for coronary artery calcium (CAC) scoring, aortic wall calcium, aortic valve calcium, mitral valve calcium, cardiac chamber volumetry (left atrium, left ventricle, right atrium, right ventricle), epicardial fat volumetry, and aorta and pulmonary artery sizing.
Arrhythmia Detection Accuracy
AI-CVD provides insights into atrial fibrillation risk through AI-driven analysis of cardiac chamber volumes, particularly enlarged left atrial volume.
Heart Failure Risk Prediction
Yes, AI-CVD predicts heart failure risk by integrating imaging disease measures, including cardiac chamber volumetry (e.g., left ventricular hypertrophy correlation with HF).
Coronary Artery Assessment
Yes, AI-CVD includes FDA-cleared modules for Coronary Artery Calcium (CAC) scoring and helps identify hidden heart risks for early intervention.
Cardiac Monitoring Integration
Not explicitly designed for integration with real-time cardiac monitoring devices; its primary function is opportunistic screening from existing CT scans.
AHA/ACC Guideline Alignment
The platform's volumetric measurements are adjusted for body surface area and reported in absolute values and population-based percentiles, referenced to studies like the Multi-Ethnic Study of Atherosclerosis (MESA) and Framingham Heart Study (FHS).
Real-Time Alert Capability
As an opportunistic screening tool, AI-CVD can issue alerts for doctors to follow up on various incidental findings, identifying patients who may warrant additional diagnostic testing, monitoring, or preventive action.
Physician Tip

Leverage AI-CVD to transform routine chest and abdominal CT scans into a powerful, scalable platform for opportunistic cardiovascular and multi-system risk assessment. Pay close attention to the quantitative measurements for CAC, cardiac chamber volumes, and valve calcifications, as these can reveal silent disease and inform early intervention strategies for conditions like heart failure, atrial fibrillation, and coronary artery disease. Remember that AI-CVD provides quantitative imaging insights and does not replace diagnostic interpretation or direct clinical judgment.

AI-CVD is designed to operate as an opportunistic add-on to CT scans already being performed for other clinical indications, such as lung cancer screening CT, diagnostic chest CT, and coronary CT angiography. It aims to integrate seamlessly into existing workflows without requiring additional imaging, radiation, contrast, or workflow disruption.

Details

Category Cardiology AI, Radiology & Imaging AI
Pricing Contact for pricing — Not available
DeploymentOn-device (integrated with Exo Iris handheld ultrasound device)
Compliance
BAA Available Yes AI-estimated
HIPAA Compliant Yes AI-estimated
FDA Status 1 AI-estimated

Exo has multiple FDA 510(k) clearances for its AI applications, including cardiac, lung, bladder, hip, and thyroid. Specifically, the Lung AI (K243239) is an FDA 510(k) cleared Computer-Aided Detection (CADe) tool designed to assist in the detection of consolidation/atelectasis and pleural effusion during lung ultrasound scans. The clearance for Lung AI was granted on April 24, 2025.

Integrations
EHR Not specified
Specialties Cardiology, Emergency Medicine, Pulmonology

What the Web Says

AI in lung health is rapidly advancing, showing significant promise in improving the diagnosis, treatment, and management of various lung conditions like cancer, asthma, COPD, and interstitial lung diseases. It assists physicians by analyzing complex patient data, interpreting imaging, and identifying subtle patterns that might be missed by the human eye, leading to earlier and more accurate diagnoses. While AI tools are not yet meant to replace human expertise, they are proving to be valuable adjuncts in clinical practice, enhancing efficiency and potentially improving patient outcomes.

Overall: Positive

Strengths

  • Improved accuracy in detecting lung nodules and other abnormalities on imaging (X-rays, CT scans, ultrasound).
  • Earlier detection of lung cancer, potentially leading to better patient outcomes and increased survival rates.
  • Assists physicians in making better diagnostic and treatment decisions by analyzing complex patient information.
  • Reduces physician workload and fatigue-related errors, allowing more time for patient care.
  • Enhances diagnostic accuracy for various lung diseases, particularly benefiting less experienced clinicians.
  • Potential for personalized treatment plans based on genomic and molecular data analysis.

Limitations

  • Potential for bias if AI is trained on incomplete or unrepresentative data, leading to unsuitable recommendations for certain patient groups.
  • Ethical considerations, regulatory hurdles, and the need for ongoing research and validation in real-world clinical settings.
  • AI tools may have lower specificity compared to radiologists in some cases, leading to false positives.
  • Integration into existing clinical workflows can be challenging, and AI prioritization alone may not always speed up the entire diagnostic pathway.
  • Lack of empathy and understanding of emotional/psychological aspects of patient experience.
  • Accessibility issues for patients without internet, smartphones, or digital skills.

Based on reviews from: Breathe, Sathyakumar, PMC, Reddit, Health IT Answers, Viz.ai, Healthcare Today, University College London Hospitals, AuntMinnie, Today's Clinical Lab, YouTube, arXiv, G2, Capterra, Blastra

Last updated: 2026-07-19

Ratings & Reviews

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Press & Coverage

AuntMinnieEurope
RevealDx secures MDR certification for RevealAI-Lung
RevealDx has received EU Medical Device Regulation (MDR) certification for its RevealAI-Lung software, which characterizes lung nodules using a Malignancy Similarity Index (mSI) to reduce diagnosis time and false positives.
2025-11
AuntMinnie
AI detects lung cancer nodules well but shows low specificity
A study published in Radiology: Artificial Intelligence found that AI models for classifying lung nodule malignancy on chest CT scans have high sensitivity but only moderate specificity, suggesting their role as adjunctive tools for ruling out malignancy rather than definitive classification.
2026-06
AuntMinnie
ASCO: With help from AI, interventions boost lung cancer screening
Research presented at ASCO demonstrated that an AI-assisted approach to lung cancer screening, combined with a centralized patient navigation model, significantly improved patient uptake of low-dose CT exams and led to earlier cancer detection.
2026-06
Brainomix / PR Newswire
Brainomix Achieves Breakthrough with FDA Clearance of e-Lung AI Software
Brainomix announced FDA clearance for its Brainomix 360 e-Lung software, marking its entry into the lung imaging space with an AI-powered solution for precision medicine.
2024-05
NIHR Journals Library
Software with artificial intelligence-derived algorithms for detecting and analysing lung nodules in CT scans: systematic review and economic evaluation
A systematic review and economic evaluation concluded that AI-assisted analysis of CT images may reduce variability and improve consistency in lung nodule management, potentially increasing nodule and cancer detection but also possibly leading to unnecessary CT surveillance.
2025-05
Mary Ann Liebert, Inc., publishers
AI Enhances Lung Ultrasound Interpretation Across Clinicians with Varying Expertise Levels
A study found that AI assistance significantly improved the sensitivity of clinicians, particularly less experienced readers, in detecting pleural effusion and consolidation/atelectasis in lung ultrasound scans, with performance approaching expert levels.
2025-08
BioIndustry Association
Brainomix e-Lung AI chosen as co-primary endpoint in Boehringer Ingelheim Phase 3 trial
Brainomix's AI-powered e-Lung software has been selected by Boehringer Ingelheim to provide objective, quantitative HRCT imaging biomarkers as a co-primary endpoint in the DROP-FPF Phase 3 study for interstitial lung disease.
2026-01
HeartLung.AI
FDA Clears HeartLung's AI-CVDu00ae, Enabling the Broadest Opportunistic Cardiovascular & Multisystem CT Screening Platform
HeartLung Corporation announced FDA clearance for AI-CVDu00ae, its AI-powered quantitative imaging platform, which enables automated extraction of cardiovascular and systemic measurements from existing CT scans for opportunistic screening.
2026-01

Videos

Product demos, reviews, and walkthroughs for Lung AI.

View all on YouTube

Frequently Asked Questions

Lung AI solutions typically integrate with Picture Archiving and Communication Systems (PACS), providing automated analysis of imaging studies like CT scans or X-rays. They can assist in detecting, quantifying, and monitoring various lung pathologies such as nodules, interstitial lung disease (ILD), COPD, and pneumonia, flagging suspicious areas for physician review. Some systems can also automate measurements and populate structured reports, improving efficiency.
Reputable Lung AI systems will have relevant regulatory clearances, such as FDA 510(k) for their specific intended uses, which should be verified. They ensure HIPAA compliance through robust data anonymization, encryption, secure data handling protocols, and adherence to strict access controls. Ethical and legal concerns like data privacy and protection are frequently reported in AI applications for lung cancer care.
While Lung AI can significantly improve efficiency by triaging cases and highlighting abnormalities, its role is generally assistive, not a replacement for human interpretation. Studies show mixed results, with some AI models demonstrating higher sensitivity but lower specificity for lung nodule detection compared to radiologists, while others show comparable or superior accuracy for specific tasks. Human oversight remains crucial for complex cases and clinical correlation.
Pricing models vary, often including subscription fees based on usage or an annual license. Reimbursement for AI-assisted diagnostics is an evolving area. Some AI tools have received Category III CPT codes (e.g., 0721T, 0722T) from the American Medical Association, which are temporary codes for emerging technologies and can facilitate reimbursement from Medicare and commercial insurers.
Lung AI can produce false positives, leading to unnecessary follow-ups, or false negatives, missing subtle findings, though some AI tools are showing promise in reducing false positives. Its performance may vary across different patient demographics, imaging protocols, and disease presentations if not adequately trained on diverse datasets, potentially introducing biases. Algorithmic bias, often stemming from the quality and robustness of training data, can lead to skewed recommendations.
Yes, many advanced Lung AI platforms offer quantitative analysis capabilities, such as measuring nodule volume, emphysema extent, or interstitial lung disease severity. This allows for objective tracking of disease progression or response to treatment over serial imaging studies, aiding in long-term patient management. AI-powered quantitative CT analysis can provide complementary insights into disease progression and treatment response.
Physicians remain ultimately responsible for patient diagnoses and treatment decisions, even when using AI as an assistive tool. The legal framework for AI liability is still evolving, but current understanding places the onus on the clinician to critically review AI outputs and integrate them into the broader clinical context. Ethical concerns also include informed consent, transparency of 'black-box' AI systems, and ensuring equitable access to AI technologies.

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Suggest an Edit → | Last Verified: 2026-04-18 | First Added: 2026-04-18
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