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.

A new paper in Biology Methods & Protocols, published by Oxford University Press, indicates that it may soon be possible for doctors to use artificial intelligence (AI) to detect and diagnose cancer in patients, allowing for earlier treatment. Cancer remains one of the most challenging human diseases, with over 19 million cases and 10 million deaths annually. The evolutionary nature of cancer makes it difficult to treat late-stage tumours.

Health Innovation East, the innovation arm of the NHS in the East of England and Cogniss, a no-code ecosystem for digital health solutions, have announced a strategic partnership to launch the Health Innovation East Digital Hub.

A no-code ecosystem is an online collection of tools, ways of working and learning resources that help people with no technical expertise build apps, in a point-and-click way, without the need for coding.