Reproduce “Towards Expert-Level Medical Question Answering with Large Language Models” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Karan Singhal, Tao Tu, Juraj Gottweis, Rory Sayres, Ellery Wulczyn, Le Hou, Kevin Clark, Stephen Pfohl and 23 more
Med-PaLM 2, an advanced large language model developed by Google Research and DeepMind, achieves 86.5% accuracy on USMLE-style questions in the MedQA dataset, approaching expert-level performance. The model generates long-form medical answers that were preferred over physician answers across multiple clinical utility dimensions in human evaluations.
Key insights
- A combination of a robust base model, domain-specific instruction finetuning, and advanced prompting techniques like 'ensemble refinement' can lead to substantial improvements in medical question answering.
- Large language models can generate long-form medical answers that are preferred by physicians over human-generated ones across various clinical utility dimensions.
- Comprehensive evaluation using human experts, adversarial testing, and multi-dimensional rubrics is critical for assessing safety, factuality, and clinical utility in high-stakes medical AI applications.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.