Researchers at McMaster University and Stanford University have invented a new generative artificial intelligence (AI) model which can design billions of new antibiotic molecules that are inexpensive and easy to build in the laboratory.

The worldwide spread of drug-resistant bacteria has created an urgent need for new antibiotics, but even modern AI methods are limited at isolating promising chemical compounds, especially when researchers must also find ways to manufacture these new AI-guided drugs and test them in the lab.

A powerful new tool in artificial intelligence is able to predict whether someone is willing to be vaccinated against COVID-19.

The predictive system uses a small set of data from demographics and personal judgments such as aversion to risk or loss.

The findings frame a new technology that could have broad applications for predicting mental health and result in more effective public health campaigns.

A mobile app that uses artificial intelligence, AI, to analyse images of suspected skin lesions can diagnose melanoma with very high precision. This is shown in a study led from Linköping University in Sweden where the app has been tested in primary care. The results have been published in the British Journal of Dermatology.

Performing a new task based solely on verbal or written instructions, and then describing it to others so that they can reproduce it, is a cornerstone of human communication that still resists artificial intelligence (AI). A team from the University of Geneva (UNIGE) has succeeded in modelling an artificial neural network capable of this cognitive prowess.

Researchers have developed a new, interpretable artificial intelligence (AI) model to predict 5-year breast cancer risk from mammograms, according to a new study published today in Radiology, a journal of the Radiological Society of North America (RSNA).

One in 8 women, or approximately 13% of the female population in the U.S., will develop invasive breast cancer in their lifetime and 1 in 39 women (3%) will die from the disease, according to the American Cancer Society.

There are thousands of diseases worldwide with no cure or available treatments. Traditional drug discovery and development takes decades and billions of dollars and more than 90% of these drugs fail in clinical trials. The emergence of artificial intelligence (AI) holds promise for streamlining and improving the entire process. However, ushering in a new era of AI-driven drug discovery requires costly and lengthy validation in preclinical cell, tissue, and animal models and human clinical trials.

When you teach a child how to solve puzzles, you can either let them figure it out through trial and error, or you can guide them with some basic rules and tips. Similarly, incorporating rules and tips into AI training - such as the laws of physics - could make them more efficient and more reflective of the real world. However, helping the AI assess the value of different rules can be a tricky task.