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.

Researchers at McMaster University have developed a new generative artificial intelligence (AI) model capable of drastically speeding up drug discovery - and, in early tests, it has already designed a brand-new antibiotic.

The discovery is a demonstration of how AI could dramatically improve the slow and costly search for new antimicrobial medicines, as bacteria and other microbes continue to evolve resistance to our current suite of drugs.

Oregon Health & Science University researchers have developed a first-of-its-kind method to predict cancer patient survival using advanced molecular data from individual cells.

Survival analysis is central to clinical oncology. Modern cancer studies can now measure gene activity in single cells from a patient’s tumor and link this information to how long patients live.