Encompassing a Dean’s Lecture Series and Dean’s Grant Program, the initiative aims to provide the institutional infrastructure and services needed to support AI use safely and effectively among faculty, staff and students.

A new artificial intelligence-based method accurately sorts cancer patients into groups that have similar characteristics before treatment and similar outcomes after treatment, according to a study led by investigators at Weill Cornell Medicine.

Leveraging the power of AI and machine learning technologies, researchers at Weill Cornell Medicine developed a more effective model for predicting how patients with muscle-invasive bladder cancer will respond to chemotherapy.

A machine-learning tool created by Weill Cornell Medicine and Hospital for Special Surgery (HSS) investigators can help distinguish subtypes of rheumatoid arthritis (RA), which may help scientists find ways to improve care for the complex condition.

Researchers at Weill Cornell Medicine have used machine learning to define three subtypes of Parkinson’s disease based on the pace at which the disease progresses.

Treatment for rheumatoid arthritis (RA) has come a long way in recent years. In many cases, a battery of medications can now successfully stymy the inflammatory cells that cause swelling and pain when they infiltrate tissues around the joints. Yet for some reason, about 20% of patients with painful, visibly swollen joints consistently get no relief from multiple rounds of even the strongest of these anti-inflammatory drugs. Surgical interventions intended to remove inflamed tissue have revealed why: “In some cases, their joints aren’t actually inflamed,” said senior author Dr. Dana Orange, an associate professor of clinical investigation in Rockefeller’s Laboratory of Molecular Neuro-oncology. “With these patients, if you press on the joint, it feels mushy and thick to the touch, but it’s not caused by the infiltrating immune cells. They have excessive tissue growth, but without inflammation. So why are they experiencing pain?” In a new paper, published April 10 in Science Translational Advances, she and her colleagues suggest an explanation. These patients have a suite of 815 genes that activate abnormal growth of sensory neurons in tissues that cushion the affected joints. “These 815 genes are rewiring the sensory nerves, which explains why anti-inflammatory drugs don’t work to alleviate pain for these patients,” Dr. Orange said. The findings may lead to new treatments for these outliers. A puzzling disconnection Rheumatoid arthritis is a tricky chronic disease. Its symptoms—stiffness, tenderness, swelling, limited motion, and pain—slowly emerge in the hands, wrists, feet, and other joints. It occurs symmetrically (not just in one hand but in both, for instance) and sporadically, with irregular flare-ups. Extreme fatigue and depression are also common. Most cases of RA are caused by products of immune cells such as cytokines, bradykinins, or prostanoids invading the synovium—a soft tissue lining the joints—where they bind to damage-sensing pain receptors. Drugs…

A team led by researchers at Weill Cornell Medicine has used an AI-based approach to uncover underlying patterns among the conditions in which people are born, grow, live, work, and age, termed social determinants of health (SDoH), and then linked each pattern to children’s health outcomes.

While the peak of the COVID-19 pandemic seems to have passed, the effects of post-COVID conditions on public health remain. A new study led by Weill Cornell Medicine and NewYork-Presbyterian investigators has found that the risk of long COVID and its symptoms present very differently across diverse populations and suggests that further investigation is needed to accurately define the disease and improve diagnosis and treatment.

The post-COVID syndrome known as long COVID has four major subtypes defined by different clusters of symptoms.

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Awards, honors, prizes and achievements for the month of December.

Weill Cornell Medicine and NewYork-Presbyterian investigators have built a machine-learning tool that can predict SARS-CoV-2 infection based on age, gender, race and 27 routine laboratory test results.