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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Evaluating Large Language Models in Extracting Cognitive Exam Dates and Scores.

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ChatGPT and LlaMA-2 were evaluated for extracting cognitive test scores from clinical notes. ChatGPT demonstrated superior accuracy in identifying MMSE and CDR scores and dates compared to LlaMA-2.

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Natural Language Processing for Clinical Data Extraction
  • Medical Informatics

Background:

  • Large language models (LLMs) are increasingly vital for medical applications.
  • Ensuring the reliability of LLMs in healthcare is critical to prevent erroneous clinical information.
  • This study focuses on the extraction of cognitive test data, specifically Mini-Mental State Examination (MMSE) and Clinical Dementia Rating (CDR) scores.

Conclusions:

  • ChatGPT demonstrates significantly higher accuracy and reliability than LlaMA-2 for extracting cognitive test scores and dates from clinical notes.
  • The findings suggest that LLMs like ChatGPT can be valuable tools in dementia research and clinical care, aiding patient identification for trials and treatment.
  • Continuous rigorous evaluation of LLM capabilities and limitations is essential for their safe and effective integration into medical practice.