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Enhancing Large Language Models for Improved Accuracy and Safety in Medical Question Answering: Comparative Study.

Dingqiao Wang1,2, Jinguo Ye1, Jingni Li1

  • 1State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.

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Summary
This summary is machine-generated.

The Med-RISE tool significantly boosts the accuracy of large language models (LLMs) in medical question answering, while also reducing inaccurate information and improving safety for clinical use.

Keywords:
ChatGPThealth care communicationlarge language modelsmedical question answeringretrieval-augmented generation

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Decision Support

Background:

  • Large language models (LLMs) show promise for healthcare but face accuracy and safety limitations.
  • Current LLMs struggle with outdated knowledge and can produce unreliable information in clinical settings.
  • Enhancing LLM reliability is critical for their adoption in healthcare.

Purpose of the Study:

  • To evaluate the Med-RISE tool's effectiveness in improving LLM accuracy and safety for medical question answering.
  • To compare Med-RISE's performance against baseline large language models (LLMs) across diverse medical domains.
  • To assess the impact of Med-RISE on reducing hallucinations in LLM-generated medical answers.

Main Methods:

  • Developed Med-RISE, a retrieval-augmented generation framework with query rewriting, real-time information retrieval, summarization, and a fact/safety filter.
  • Integrated Med-RISE with four LLMs (GPT-3.5, GPT-4, Vicuna-13B, ChatGLM-6B).
  • Assessed performance on four medical question datasets (MedQA, PubMedQA, MedMCQA, EYE500), measuring accuracy and hallucination rates (factuality and faithfulness).

Main Results:

  • Med-RISE integration increased LLM accuracy by 9.8%–16.3% (mean 13%) across datasets.
  • Accuracy improvements were 16.3% for GPT-3.5, 12.9% for GPT-4, 13% for Vicuna-13B, and 9.8% for ChatGLM-6B.
  • Med-RISE reduced hallucinations by 11.8%–18% (mean 15.1%), with factuality hallucinations decreasing by 13.5% and faithfulness by 5.8%.

Conclusions:

  • The Med-RISE framework substantially enhances LLM accuracy and reduces hallucinations in medical question answering.
  • Real-time retrieval and filtering capabilities improve LLM reliability and interpretability in medicine.
  • Med-RISE presents a promising solution for clinical practice and decision support systems.