Many AI models fail to recognise negation words such as “no” and “not”, which means they can’t easily distinguish between medical images labelled as showing a disease and images labelled ...

In this study, a robust automated system for TB detection from chest X-ray images using deep learning techniques is developed. The dataset, sourced from Kaggle, comprises 4200 images divided into two ...

Task, sample, behavioral performance, and electrode coverage. Credit: Science Advances (2025). DOI: 10.1126/sciadv.adp9336 ...

Getty Images holds and licenses more than 572 million “visual assets” and more than 200 million of these are made available for licensing either for free or with a paid subscription.

Getty Images is going all in to establish itself as a trusted data partner. The creative company, known for enabling the sharing, discovery and purchase of visual content from global photographers ...

Yuzhe Yang, Haoran Zhang, Judy W. Gichoya, Dina Katabi, Marzyeh Ghassemi. The limits of fair medical imaging AI in real-world generalization. Nature Medicine, 2024; DOI: 10.1038/s41591-024-03113-4 ...

Massive amounts of data make medical imaging ripe for AI It’s not unusual for Langlotz to arrive at the hospital early on a Saturday morning to find 150 patient images waiting for him to review.

In their experiments, they introduced a new dataset, Probing Evaluation for Medical Diagnosis (ProbMed), for which they curated 6,303 images from two widely-used biomedical datasets.

Taking a step in this direction, MIT researchers developed an image dataset that allows them to simulate peripheral vision in machine learning models.

Instead, an AI model can pre-scan medical images and flag those containing something unusual - like a tumor or early sign of disease, called a biomarker - for a doctor's review.