A new artificial intelligence (AI) tool that can help to select the most suitable treatment for cancer patients has been developed by researchers at The Australian National University (ANU).

DeepPT, developed in collaboration with scientists at the National Cancer Institute in America and pharmaceutical company Pangea Biomed, works by predicting a patient's messenger RNA (mRNA) profile.

Osteoporosis is so difficult to detect in early stage it’s called the "silent disease." What if artificial intelligence could help predict a patient’s chances of having the bone-loss disease before ever stepping into a doctor's office?

Tulane University researchers made progress toward that vision by developing a new deep learning algorithm that outperformed existing computer-based osteoporosis risk prediction methods, potentially leading to earlier diagnoses and better outcomes for patients with osteoporosis risk.

Artificial intelligence (AI) models often play a role in medical diagnoses, especially when it comes to analyzing images such as X-rays. However, studies have found that these models don’t always perform well across all demographic groups, usually faring worse on women and people of color.

These models have also been shown to develop some surprising abilities.

Proteins are the building blocks of life, involved in virtually every biological process. Understanding how proteins interact with each other is crucial for deciphering the complexities of cellular functions, and has significant implications for drug development and the treatment of diseases.

However, predicting which proteins bind together has been a challenging aspect of computational biology, primarily due to the vast diversity and complexity of protein structures.

Meet CARMEN, short for Cognitively Assistive Robot for Motivation and Neurorehabilitation - a small, tabletop robot designed to help people with mild cognitive impairment (MCI) learn skills to improve memory, attention, and executive functioning at home.

Unlike other robots in this space, CARMEN was developed by the research team at the University of California San Diego in collaboration with clinicians, people with MCI, and their care partners.

Nearly all the neural networks that power modern artificial intelligence (AI) tools such as ChatGPT are based on a 1960s-era computational model of a living neuron. A new model developed at the Flatiron Institute's Center for Computational Neuroscience (CCN) suggests that this decades-old approximation doesn’t capture all the computational abilities that real neurons possess and that this older model is potentially holding back AI development.

A new AI-powered program will allow researchers to level up their drug discovery efforts.

The program, called TopoFormer, was developed by an interdisciplinary team led by Guowei Wei, a Michigan State University Research Foundation Professor in the Department of Mathematics. TopoFormer translates three-dimensional information about molecules into data that typical AI-based drug-interaction models can use, expanding those models' abilities to predict how effective a drug might be.