This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility:

The aging population, workforce changes and patients' increasingly complex medical needs are all factors straining primary care systems around the world. Artificial intelligence tools have emerged as a potential means of extending the capabilities of health care workers, including in primary care settings. Large language models (LLMs), also known as chatbots, have performed extremely well in simulations. However, little is known about how these systems perform with real patients.

Researchers at Beth Israel Deaconess Medical Center (BIDMC) have conducted what they believe is the first prospective real-world study of a patient-facing conversational AI system in primary care, taking an important step toward evaluating the technology through the same kind of rigorous clinical testing used for other health care interventions. The findings are published in The Lancet.

"Despite the theoretical performance of patient-facing AI in experimental settings, it is unclear how conversationally safe these encounters are in a real-world setting," said Adam Rodman, M.D., director of AI programs at the Carl J. Shapiro Institute for Research and Education at BIDMC. "Before we begin evaluating them as workload interventions, AI tools must first be shown to operate safely and acceptably in real care workflows. With this research, we set out to assess whether this kind of technology could improve both patient and physician experience."

Rodman and colleagues, in collaboration with researchers at Google, evaluated the Articulate Medical Intelligence Explorer (AMIE), a medical AI chatbot that patients used from home after scheduling an urgent primary care appointment with a BIDMC physician.

Source link