A groundbreaking machine-learning study has unmasked the best drug combinations to prevent COVID-19 from coming back after an initial infection. It turns out these combos are not the same for every patient.

Using real-world data from a hospital in China, the UC Riverside-led study found that individual characteristics, including age, weight, and additional illness determine which drug combinations most effectively reduce recurrence rates.

Engineers at the University of California San Diego have developed a simple, low-cost clip that uses a smartphone's camera and flash to monitor blood pressure at the user's fingertip. The clip works with a custom smartphone app and currently costs about 80 cents to make. The researchers estimate that the cost could be as low as 10 cents apiece when manufactured at scale.

Huge libraries of drug compounds may hold potential treatments for a variety of diseases, such as cancer or heart disease. Ideally, scientists would like to experimentally test each of these compounds against all possible targets, but doing that kind of screen is prohibitively time-consuming.

In recent years, researchers have begun using computational methods to screen those libraries in hopes of speeding up drug discovery.

An artificial intelligence (AI) computer program can read physicians’ notes to accurately estimate patients' risk of death, length of hospital stay, and other factors important to care. Designed by a team led by researchers at NYU Grossman School of Medicine, the tool is currently in use in its affiliated hospitals to predict the chances that a patient who is discharged will be readmitted within a month.

Heather Desaire, a chemist who uses machine learning in biomedical research at the University of Kansas, has unveiled a new tool that detects with 99% accuracy scientific text generated by ChatGPT, the artificial intelligence text generator.

The peer-reviewed journal Cell Reports Physical Science published research showing the efficacy of her AI-detection method, along with sufficient source code for others to replicate the tool.

While it can take years for the pharmaceutical industry to create medicines capable of treating or curing human disease, a new study suggests that using generative artificial intelligence could vastly accelerate the drug-development process.

Today, most drug discovery is carried out by human chemists who rely on their knowledge and experience to select and synthesize the right molecules needed to become the safe and efficient medicines we depend on.

Five subtypes of heart failure that could potentially be used to predict future risk for individual patients have been identified in a new study led by UCL researchers.

Heart failure is an umbrella term for when the heart is unable to pump blood around the body properly. Current ways of classifying heart failure do not accurately predict how the disease is likely to progress.