When can we trust the results we get from AI, and when is learning impossible? Researchers have shown that there are some problems that even the most powerful AI can reliably solve, no matter how much data it’s given.

The researchers, from the University of Cambridge and the University of California Santa Barbara, designed ‘adversarial’ mathematical systems designed to fool any AI algorithm.

A new review in Advanced Cancer Research explains how artificial intelligence (AI) is being used across cancer drug discovery, from finding targets to designing and testing new drug candidates. The authors show where AI is already useful, and what still needs to improve before more AI-designed therapies can reach patients.

Rooted in thousands of years of clinical practice and holistic philosophy, Traditional Chinese Medicine (TCM) features a distinctive therapeutic model of “multicomponent, multitarget, multipathway” intervention guided by syndrome differentiation. This approach shows unique advantages in treating complex diseases including cancer, metabolic disorders, and infectious diseases. However, this inherent complexity also creates a major barrier to modern scientific interpretation:

In experiments in which physicians made decisions about treating hypothetical patients, the physicians tended to trust incorrect advice presented as being generated by artificial intelligence (AI), even after given the opportunity to notice that patient recovery data contradicted the recommendations. Aranzazu Vinas of the University of the Basque Country, Spain, and colleagues present these findings in the open-access journal PLOS Digital Health.

Researchers at Stanford University have announced the debut of Biomni - an AI-powered multi-skilled biomedical research agent. Biomni is no mere chatbot. It is a full-fledged “co-scientist” capable of designing and developing complex research workflows, said Jure Leskovec, the Alfred and Rebecca Lin Professor and professor of computer science in the School of Engineering and senior author of the paper introducing Biomni in the journal Science.

Tuberculosis, caused by the bacterium Mycobacterium tuberculosis (Mtb) is the world’s deadliest single-agent caused infection, responsible for 1.23 million deaths in 2024, according to the World Health Organization. The bacterium’s unique outer cell membrane is notoriously hard to penetrate, making few drugs, including antibiotics, effective in treating the disease.

Brown University researchers have developed a new artificial intelligence method for predicting the rate at which materials used in controlled drug-release systems will release therapeutic agents.

The new method could slash the development time for new therapeutic patches, bandages and implants.