Bayer and U.K.-based artificial intelligence (AI) software developer Huma are joining forces to develop AI technology to distinguish different forms of non-small-cell lung cancer (NSCLC) on CT exams.
Making use of Huma's machine-learning experience and Bayer's oncology and medical imaging capabilities, the companies plan to utilize machine-learning technology to spot correlations in molecular and imaging assessments -- such as ground-glass opacities -- that can differentiate types of lung cancers. They will then train and test models to provide accurate diagnoses.
The goal is to quickly identify the patients with certain types of NSCLC who can benefit the most from tailored treatments, according to the firms. Their collaboration will begin immediately.
Bayer's Leaps by Bayer investment arm has been both a series B and series C investor in Huma.
![Axial images from unenhanced calcium score cardiac CT (left) and curved planar reformation images from CT angiography (right) show that higher long-term exposure to air pollution is associated with greater coronary artery calcium and more obstructive coronary artery disease (CAD). Top row: Images in a 68-year-old male patient with higher 10-year mean ambient air pollution exposure (7.9 μg/m3 for particulate matter measuring ≤2.5 μm in diameter [PM2.5] and 17.4 parts per billion [ppb] for NO2) with extensive CAD (coronary artery calcium score [CACS] >1,000 and obstructive CAD [≥70% diameter stenosis]). Bottom row: Images in a 57-year-old female patient with lower 10-year mean ambient air pollution exposure (6.3 μg/m3 for PM2.5 and 4.6 ppb for NO2) with no CAD (CACS = 0 and no obstructive stenosis).](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/06/hanneman.r6SMLzkezo.png?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
