Publications

With the only FDA-cleared, Medicare-reimbursable solution for AI-powered lung cancer diagnostic support, our technology is built on robust scientific & clinical evidence. Published in leading journals and collaborating with top academics & clinicians working in thoracic care, our publications form the foundation of our platform advancing precision care for lung cancer & lung disease

Publication type Conference proceedings Peer-reviewed paper

Evidence type Clinical utility Clinical validity & accuracy Health economic outcomes

Conference proceedings Clinical utility

Real-world integration of artificial intelligence for lung cancer prediction of

Miller et al 2026

Publications

Category

Real-world integration of artificial intelligence for lung cancer prediction of

Cost-effectiveness analysis of artificial intelligence-assisted risk stratification of indeterminate pulmonary nodules

Theoretical Clinical Utility of Advanced Practice Provider Use of an Artificial Intelligence Radiomics-based Tool...

Employment of Artificial Intelligence for Early Lung Cancer Diagnosis: A Retrospective Cohort Study

Impact of Artificial Intelligence (AI) on Follow-up of Incidental Lung Nodules: A US Multi-center...

Artificial intelligence-enabled lung nodule clinic to enhance clinical follow-up and early-stage lung cancer detection

Radiomic 'Stress Test': Exploration of a deep learning radiomic model in a high-risk prospective...

Incidentals Upon Incidentals: Characterizing and Quantifying Significant Radiologic Findings Within a Centralized Incidental Lung...

Can an Artificial Intelligence-supported Incidental Nodule Program Outperform a Lung Cancer Screening Program?

Theoretical Clinical Utility of Advanced Practice Provider Use of an Artificial Intelligence Radiomics-Based Tool...

Identifying Patients With Pulmonary Masses From CT Radiology Reports Using Natural Language Processing (NLP)

Leveraging Artificial Intelligence as a Safety Net for Incidentally Identified Lung Nodules at a...

Malignancy risk stratification for pulmonary nodules: comparing a deep learning approach to multiparametric...

Utilization of artificial intelligence support for early lung cancer diagnosis in a lung nodule...

Successful implementation of Artificial Intelligence (AI) to Support a Lung Nodule Program in Driving...

Clinical utility of an artificial intelligence radiomics-based tool for risk stratification of pulmonary nodules

Pre- and post-operative lung cancer recurrence prediction following curative surgery: A retrospective study using...

Deep Learning Models for Predicting Malignancy Risk in CT-Detected Pulmonary Nodules: A Systematic Review...

Longitudinal Trend of a Radiomic Lung Cancer Prediction Score for Sub-solid Nodules

Determining the impact of an artificial intelligence tool on the management of pulmonary nodules...

Prediction of lung cancer risk in ground-glass nodules using deep learning from CT images

Identifying Patients With Pulmonary Nodules From CT Radiology Reports Using Natural Language Processing (NLP)

Artificial Intelligence-assisted Versus Clinician Only Evaluation of Indeterminate Pulmonary Nodules: A Comparative Effectiveness Study

Longitudinal lung cancer prediction convolutional neural network model improves the classification of indeterminate pulmonary...

Effect of an artificial intelligence tool on management decisions for indeterminate pulmonary nodules

Recalibration of a Deep Learning Model for Low-Dose Computed Tomographic Images to Inform Lung...

Diagnostic Accuracy of a Convolutional Neural Network Assessment of Solitary Pulmonary Nodules Compared With...

Artificial Intelligence Tool for Assessment of Indeterminate Pulmonary Nodules Detected with CT

Utilizing a Deep Learning Radiomic Model for the Quantification of Emphysema

Developing an understanding of artificial intelligence lung nodule risk prediction using insights from the...

An Artificial Intelligence Lung Cancer Prediction Tool Improves Clinicians' Risk Assessment of Indeterminate Pulmonary...

Lung cancer prediction by Deep Learning to identify benign lung nodules

The utility of a convolutional neural network (CNN) model score for cancer risk in...

AI Assistance for Pulmonary Nodule Stratification: An Multiple-Reader Multiple-Case Study

Evaluation of a novel deep learning–based classifier for perifissural nodules

AI-Based Computer-Aided Diagnosis (CADx) Improves Stratification Decisions on Indeterminate Pulmonary Nodules: An MRMC Reader...

Assessing the accuracy of a deep learning method to risk stratify indeterminate pulmonary nodules

External validation of a convolutional neural network artificial intelligence tool to predict malignancy in...

How Many Malignancies Present at Baseline That Get Diagnosed in the Next Screening Round?...

The Utility of a Convolutional Neural Network (CNN) Model Score for Cancer Risk in...

Evaluation of a Deep Learning-Based Automatic Classifier for the Classification of Perifissural Nodules

What Is the Impact of Localised Data When Training Deep Neural Networks for Lung...

Acceleration of lung cancer diagnosis: utility study for AI-based stratification of pulmonary nodules

Repeated Measures of Risk Based on Radiomics Features of Indeterminate Pulmonary Nodules for the...

Multi-Center Independent Validation of AI Risk Stratification for Indeterminate Pulmonary Nodules from CT Images

Deep Learning for Rule-Out of Unnecessary Follow-Up in Patients with Incidentally Detected, Indeterminate Pulmonary...

Nodule Size Measurement: Automatic or Human-Which is Better for Predicting Lung Cancer in a...

Automatic Nodule Size Measurements Can Improve Prediction Accuracy Within a Brock Risk Model

Lung Cancer Prediction Using Deep Learning Software: Validation on Independent Multi-Centre Data

AI Based Malignancy Prediction of Indeterminate Pulmonary Nodules: Robustness to CT Contrast Media

Solid and Part-Solid Lung Nodule Classification Using Deep Learning on the National Lung Screening...

Deep Learning Based Risk Stratification of Patients with Suspicious Nodules

Pulmonary nodules: Assessing the imaging biomarkers of malignancy in a "coffee-break"

Lung nodule risk stratification using Deep Learning on the complete US National Lung Screening...

Assessment of CT Texture Analysis as a Tool for Lung Nodule Follow-Up

A Comparison of the Imaging Features of Early Stage Primary Lung Cancer in Patients...

Nodule Size Isn't Everything: Imaging Features Other Than Size Contribute to AI Based Risk...

A retrospective validation study of three models to estimate the probability of malignancy in patients with small pulmonary nodules from a tertiary oncology follow-up centre