Authors: Ofir Ben Shoham, Nadav Rappoport

Published on: May 12, 2024

Impact Score: 7.4

Arxiv code: Arxiv:2405.07348

Summary

• What is new: Introduction of MedConceptsQA, a new benchmark specifically for evaluating Large Language Models on medical concepts question answering across different medical vocabularies and difficulty levels.
• Why this is important: Existing benchmarks do not adequately test Large Language Models’ ability in understanding diverse and complex medical concepts.
• What the research proposes: Creation of MedConceptsQA benchmark that includes a wide range of medical questions categorized into different levels of difficulty and covering various medical vocabularies.
• Results: GPT-4 outperformed pre-trained clinical language models by achieving significantly higher accuracy, showing improvements of 27%-37% over these models.

Technical Details

Technological frameworks used: Open source benchmark

Models used: Large Language Models, GPT-4, clinical Language Models

Data used: Variety of medical concepts including diagnoses, procedures, and drugs

Potential Impact

Healthcare and medical research sectors, AI development companies focusing on healthcare applications, medical education and training platforms

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