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.

AI models - for example, those used for cancer detection - are trained on patients’ health data. Even the mere fact that personal data has been incorporated into a model can have negative consequences for those affected if this information falls into the wrong hands. In Nature, a research team now shows that, using the right methods, this sensitive information can be extracted from models far more effectively than previously thought.

An AI model (REDMOD) can pick up the very early subtle tissue changes of pancreatic ductal adenocarcinoma, the most common form of pancreatic cancer, which conventional imaging and the human eye find difficult to detect, finds research published online in the journal Gut.

As such, it offers the potential to shift an all too common late stage, terminal disease diagnosis to one that is at an early stage (stage 0) and treatable, say the researchers.

Attention-deficit/hyperactivity disorder (ADHD) affects millions of children, yet many go years without a diagnosis, missing the chance for early support that can change long-term outcomes even when early signs are present.

In a new study, Duke Health researchers found that artificial intelligence tools can analyze routine electronic health records to accurately estimate a child’s risk of developing ADHD years before a typical diagnosis.

Digital transformation and artificial intelligence (AI) in healthcare requires a range of safeguards and standards to work well, but new research from Flinders University provides support for effective AI systems to improve cardiovascular care.

The study examines how Clinical Decision Support Systems (CDSS) can transform cardiovascular disease management - a leading cause of death in Australia - by providing more accurate, timely decisions while addressing real-world barriers like workflow integration, usability and clinician adoption.