Scientists at ARUP Laboratories have developed an artificial intelligence (AI) tool that detects intestinal parasites in stool samples more quickly and accurately than traditional methods, potentially transforming how labs diagnose parasitic infections around the world.

Identifying parasites under the microscope has long been a painstaking task requiring highly trained experts to manually scour each sample for telltale cysts, eggs or larva.

A study published in the British Journal of Health Psychology reveals the negative behavioral and psychological consequences of commercial fitness apps reported by users on social media. These impacts may undermine the potential of apps to promote health and wellbeing.

When investigators used artificial intelligence (AI) using a method called Machine-Assisted Topic Analysis (MATA), which combines AI-powered topic modelling with human qualitative analysis,

Breast cancer is the most commonly diagnosed form of cancer in the world among women, with more than 2.3 million cases a year, and continues to be one of the main causes of cancer-related mortality. Precisely predicting whether this type of tumour will reappear remains one of the key challenges in oncology. To try and make progress in this field, an international team led by the Universitat Rovira i Virgili has developed an artificial intelligence model that brings together medical imaging data and clinical information to calculate the risk of tumour recurrence in a much more accurate and interpretative way.

A landmark study led by University' experts has shown that artificial intelligence can better predict how doctors should treat patients following a heart attack.

The study, conducted by an international team of researchers, led by the University of Leicester’s Honorary fellow, Doctor Florian Wenzl working closely with Professor David Adlam, both from the Department of Cardiovascular Sciences, has been published in The Lancet Digital Health.

Doctors often must make critical decisions in minutes, relying on incomplete information. While electronic health records contain vast amounts of patient data, much of it remains difficult to interpret quickly - especially for patients with rare diseases or unusual symptoms.

Now, researchers at the Icahn School of Medicine at Mount Sinai and collaborators have developed an artificial intelligence system, called InfEHR, that links unconnected medical events over time, creating a diagnostic web that reveals hidden patterns.

When doctors analyze a medical scan of an organ or area in the body, each part of the image has to be assigned an anatomical label. If the brain is under scrutiny for instance, its different parts have to be labeled as such, pixel by pixel: cerebral cortex, brain stem, cerebellum, etc. The process, called medical image segmentation, guides diagnosis, surgery planning and research.

Artificial intelligence (AI) carries promise and uncertainty for clinicians, patients, and health systems. This JAMA Summit Report presents expert perspectives on the opportunities, risks, and challenges of AI in health care, including how AI is developed, evaluated, regulated, and implemented across clinical and business domains.