Low-dose computed tomography (LDCT) is an effective screening tool for early lung cancer detection; however, it frequently reveals incidental findings (IFs) beyond suspicious pulmonary nodules. We ...
We combined natural language processing and large language models with state-of-the-art machine learning techniques and approaches to treat unbalanced data sets and determine the best solution to ...
Lung cancer remains one of the malignancies with the highest incidence and mortality worldwide. Clinical practice has long been plagued by core dilemmas, including insufficient sensitivity in early ...
AI-assisted spirometry, imaging biomarkers, and population screening strategies highlighted early detection and prevention as a throughline at the ERS Congress. Artificial intelligence–supported ...
Development and validation of an artificial intelligence–based deep learning imaging model for early lung cancer detection: DAVINCI, a retrospective study of 8,962 patients. This is an ASCO Meeting ...
Machine learning models can predict urgent care needs for patients with non–small cell lung cancer
A new study published in JCO Clinical Cancer Informatics demonstrates that machine learning models incorporating patient-reported outcomes and wearable sensor data can predict which patients with ...
The future of machine learning in Canadian medical diagnostics appears increasingly promising. Advances in computing power, ...
Data presented at the 2026 AACR Annual Meeting demonstrated the promise of blood-based lung cancer detection via SimpleScreen Lung. Developers designed SimpleScreen Lung as an artificial intelligence ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results