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.

Artificial intelligence is spreading rapidly in health care, with the goal of streamlining critical but onerous clerical tasks such as note-taking and charting so that physicians and nurses can devote more time to patients.

But even when AI can free up doctors to correspond with patients, it may fall short in helping them do it by introducing errors and extraneous details into their messages, according to a new Dartmouth study presented at the 2026 Annual Meeting of the Association for Computational Linguistics and published in the conference proceedings.

Researchers from The University of Texas MD Anderson Cancer Center demonstrated that an artificial intelligence (AI)-based analysis of tumor biopsies can predict responses to immunotherapy in a study of patients with rare cancers, published in the Journal for ImmunoTherapy of Cancer.

Led by Aung Naing, M.D., professor of Investigational Cancer Therapeutics, this analysis builds on recently published research that identified features in the tumor microenvironment that were predictive of immunotherapy response in patients with rare cancers, even in those who did not have known markers of immunotherapy response.