A new study found that artificial intelligence can better identify instances of physical child abuse in emergency rooms compared to traditional diagnostic coding methods.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition in which affected individuals experience difficulties in social communication and exhibit restricted, repetitive patterns of behavior or interests.

A growing body of research suggests that neurobiological changes, particularly abnormalities in dendritic spines, tiny protrusions on nerve cells where synapses form, may be a hallmark of ASD. In particular, studies have found an unusually high number of these spines in individuals with autism. This overabundance of synaptic connections could disrupt normal communication pathways in the brain, potentially contributing to the behavioral and cognitive features seen in ASD.

Under normal circumstances, the brain undergoes synaptic pruning, a process involving the removal of unnecessary or weak synaptic connections to make way for more efficient neural networks. This pruning is crucial during early development and adolescence.

Xiaomi Corp. unveiled its own open-source AI model, joining the growing ranks of Chinese tech leaders hoping to make a splash in a burgeoning field endorsed by Beijing.

An accurate diagnosis of ADHD is crucial in bringing clarity and the right support to people who need it, but current diagnosis methods are time-consuming and inconsistent. A new study suggests AI could help.

Researchers in South Korea trained machine learning models to connect characteristics in photos of the fundus at the back of the eye to a professional diagnosis of ADHD (attention deficit hyperactivity disorder).

Of four machine learning models tested in the study, the best achieved a 96.9 percent score for predicting ADHD accurately, based on image analysis alone.

NVIDIA’s AI Blueprint combines Black Forest Labs’ FLUX.1-dev as an NVIDIA NIM microservice, ComfyUI and Blender in a prebuilt workflow optimized for RTX AI PCs.