A constantly updated RAG architecture mitigates the risks of a stale AI system by ensuring AI outputs reflect the latest ...

Traditionally, drug discovery relied heavily on trial and error, with long timelines and high costs. The introduction of ...

AI could save lives and billions in health care, but adoption is slowed by ethical, technical, and regulatory challenges, keeping changes incremental.

These capabilities illustrate the multifaceted promise of AI: speeding up scientific discovery, improving patient outcomes, ...

The announcement was the latest move by the Trump Administration to emphasize the value of incorporating the latest ...

Health care can rapidly advance with innovations incorporating artificial intelligence (AI). As a leading partner in New York State's Empire AI consortium, UB is pursuing new technology and data ...

AI-driven human resources systems used for hiring, scheduling, or staff evaluations can inadvertently discriminate against specific demographics, affecting the diversity of health care providers ...

Surya Gummadi, president of Cognizant Americas, discusses the transformative impact of AI in health care, emphasizing advancements in drug discovery, clinical trials and cost reduction.

Health care providers face their own challenges, from patient loads to paperwork. UC San Francisco Clinical Associate Professor of Medicine Sara Murray, MD, MAS, studies how technology and data shape ...

When assessing the utility of AI in health care, it’s important to look at each AI tool separately and to understand how it was developed and in what circumstances they are meant to be used.

The Coalition for Health AI (CHAI) is building off positive feedback from its applied model cards—or AI "nutrition labels" —by giving them a more sophisticated home.

And although 43% of U.S. health care organizations had added or expanded AI use in 2024, many implementations are still exploratory, particularly when it comes to medical decisions and diagnoses.