Explore more about: Artificial Intelligence

NIBIB-funded researchers are developing an autonomous robot that can perform bowel surgery with minimal assistance from a surgeon. In preclinical models, the robot outperformed expert surgeons when compared head-to-head.

Artificial intelligence (AI) models that evaluate medical images have potential to speed up and improve accuracy of cancer diagnoses, but they may also be vulnerable to cyberattacks. Researchers simulated an attack that falsified mammogram images, fooling both an AI breast cancer diagnosis model and human breast imaging radiologist experts.

NIH-funded researchers are investigating how to use smartwatches to predict clinical test results, which could potentially serve as an early warning signal for underlying health issues.

A team led by NIBIB scientists has developed hardware and software innovations to construct super-resolution, 3D confocal images of fine structures in living samples.

NIBIB-funded engineers are using deep learning to differentiate tumor more accurately from normal tissue in positron emission tomography (PET) images.

A team of NIH microscopists and computer scientists used a type of artificial intelligence called a neural network to obtain clearer pictures of cells at work even with extremely low, cell-friendly light levels.

The Medical Imaging and Data Resource Center (MIDRC), a collaboration of leading medical imaging organizations funded by NIBIB, has launched a repository of imaging data to facilitate medical imaging research on COVID-19.

Compared to standard machine learning models, deep learning models are largely superior at discerning patterns and discriminative features in brain imaging, despite being more complex in their architecture.

A team of engineers has demonstrated how a new algorithm they developed was able to successfully predict whether or not a COVID-19 patient would need ICU intervention. This artificial intelligence-based approach could be a valuable tool in determining a proper course of treatment for individual patients.

A new study lays out a large medical analytics framework that can be used in neuroscience and neurology to study brain connectivity in living organisms.