Search AI Products and News

Yale University researchers have developed MindLLM, an AI model capable of translating fMRI brain signals directly into text. This groundbreaking technology bridges the gap between brain activity and human language, opening new possibilities for communication and understanding the human brain.

Analyzing medical image data has always been a complex and laborious process. Recently, researchers at Weill Cornell Medicine developed a novel artificial intelligence system called LILAC (Learning-based Inference of Longitudinal Image Changes) that can efficiently and accurately analyze and detect changes in medical images over time. This research, published on February 20th in the Proceedings of the National Academy of Sciences, showcases LILAC's broad application potential in various medical scenarios. Traditional medical image analysis methods often require extensive customization and pre-processing.

MRI images have long been a significant challenge in medical imaging analysis due to their complexity and large data volume. To train large language models (LLMs) for MRI analysis, developers have had to slice the captured images into 2D images. While this approach is feasible, it limits the model's ability to analyze complex anatomical structures, particularly in intricate cases such as brain tumors, skeletal diseases, or cardiovascular conditions. Image source note: Image generated by AI, licensed by Midjourney. However, GE Healthcare this year...

Last year, Germany and France enacted laws banning the culling of male chicks, prompting tech companies to develop methods for determining chick gender. Orbem, a startup based in Munich, combines MRI and AI technology to determine the gender of an egg in under a second. Orbem has scanned 20 million eggs and plans to expand its technology application to human MRI. It is predicted that the global AI medical imaging market will continue to see the emergence of startups utilizing MRI and AI technology in the future.