Towards Expert-Level Medical Question Answering with Large Language
  Models
Authors: name: Karan Singhal, name: Tao Tu, name: Juraj Gottweis, name: Rory Sayres, name: Ellery Wulczyn, name: Le Hou, name: Kevin Clark, name: Stephen Pfohl, name: Heather Cole-Lewis, name: Darlene Neal, name: Mike Schaekermann, name: Amy Wang, name: Mohamed Amin, name: Sami Lachgar, name: Philip Mansfield, name: Sushant Prakash, name: Bradley Green, name: Ewa Dominowska, name: Blaise Aguera y Arcas, name: Nenad Tomasev, name: Yun Liu, name: Renee Wong, name: Christopher Semturs, name: S. Sara Mahdavi, name: Joelle Barral, name: Dale Webster, name: Greg S. Corrado, name: Yossi Matias, name: Shekoofeh Azizi, name: Alan Karthikesalingam, name: Vivek Natarajan

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.
  Large language models (LLMs) have catalyzed significant progress in medical
question answering; Med-PaLM was the first model to exceed a "passing" score in
US Medical Licensing Examination (USMLE) style questions with a score of 67.2%
on the MedQA dataset. However, this and other prior work suggested significant
room for improvement, especially when models' answers were compared to
clinicians' answers. Here we present Med-PaLM 2, which bridges these gaps by
leveraging a combination of base LLM improvements (PaLM 2), medical domain
finetuning, and prompting strategies including a novel ensemble refinement
approach.
  Med-PaLM 2 scored up to 86.5% on the MedQA dataset, improving upon Med-PaLM
by over 19% and setting a new state-of-the-art. We also observed performance
approaching or exceeding state-of-the-art across MedMCQA, PubMedQA, and MMLU
clinical topics datasets.
  We performed detailed human evaluations on long-form questions along multiple
axes relevant to clinical applications. In pairwise comparative ranking of 1066
consumer medical questions, physicians preferred Med-PaLM 2 answers to those
produced by physicians on eight of nine axes pertaining to clinical utility (p
< 0.001). We also observed significant improvements compared to Med-PaLM on
every evaluation axis (p < 0.001) on newly introduced datasets of 240 long-form
"adversarial" questions to probe LLM limitations.
  While further studies are necessary to validate the efficacy of these models
in real-world settings, these results highlight rapid progress towards
physician-level performance in medical question answering.