Towards Expert-Level Medical Question Answering with Large Language Models
Karan Singhal Affiliation: Google Research, Tao Tu Affiliation: Google Research, Juraj Gottweis Affiliation: Google Research, Rory Sayres Affiliation: Google Research, Ellery Wulczyn Affiliation: Google Research, Le Hou Affiliation: Google Research, Kevin Clark Affiliation: Google Research, Stephen Pfohl Affiliation: Google Research, Heather Cole-Lewis Affiliation: Google Research, Darlene Neal Affiliation: Google Research, Mike Schaekermann Affiliation: Google Research, Amy Wang Affiliation: Google Research, Mohamed Amin Affiliation: Google Research, Sami Lachgar Affiliation: Google Research, Philip Mansfield Affiliation: Google Research, Sushant Prakash Affiliation: Google Research, Bradley Green Affiliation: Google Research, Ewa Dominowska Affiliation: Google Research, Blaise Aguera y Arcas Affiliation: Google Research, Nenad Tomasev Affiliation: DeepMind, Yun Liu Affiliation: Google Research, Renee Wong Affiliation: Google Research, Christopher Semturs Affiliation: Google Research, S. Sara Mahdavi Affiliation: Google Research, Joelle Barral Affiliation: Google Research, Dale Webster Affiliation: Google Research, Greg S. Corrado Affiliation: Google Research, Yossi Matias Affiliation: Google Research, Shekoofeh Azizi Affiliation: Google Research, Alan Karthikesalingam Affiliation: Google Research, Vivek Natarajan Affiliation: Google Research,
Abstract
Recent artificial intelligence (AI) systems have reached milestones in “grand challenges” ranging from Go to protein-folding. The capability to retrieve medical knowledge, reason over it, and answer medical questions comparably to physicians has long been viewed as one such grand challenge.
原文 arXiv:2305.09617;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2305.09617v1