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.

Researchers have improved the ability of wearable health devices to accurately detect when a patient is coughing, making it easier to monitor chronic health conditions and predict health risks such as asthma attacks. The advance is significant because cough-detection technologies have historically struggled to distinguish the sound of coughing from the sound of speech and nonverbal human noises.

Although artificial intelligence (AI) has already shown promise in cardiovascular medicine, most existing tools analyze only one type of data - such as electrocardiograms or cardiac images - limiting their clinical utility. The emergence of multimodal AI, which fuses information from multiple sources, now allows algorithms to mimic the holistic reasoning of cardiologists and deliver more accurate, patient-specific insights.

Researchers explore the human immune system by looking at the active components, namely the various genes and cells involved. But there is a broad range of these, and observations necessarily produce vast amounts of data. For the first time, researchers including those from the University of Tokyo built a software tool which leverages artificial intelligence to not only offer a more consistent analysis of these cells at speed but also categorizes them and aims to spot novel patterns people have not yet seen.

Artificial intelligence (AI) can solve problems at remarkable speed, but it’s the people developing the algorithms who are truly driving discovery. At The University of Texas at Arlington, data scientists are creating sophisticated formulas that enable AI to interpret massive biological datasets to uncover how diseases start, how the immune system responds, and what treatments might work best.