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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Related Experiment Video

Updated: Jun 9, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Benchmarking Large Language Models in Evidence-Based Medicine.

Jin Li, Yiyan Deng, Qi Sun

    IEEE Journal of Biomedical and Health Informatics
    |October 22, 2024
    PubMed
    Summary

    Large language models (LLMs) show promise for enhancing evidence-based medicine (EBM) by automating tasks like evidence retrieval and summarization. However, challenges with factual accuracy require further research before clinical use.

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    Area of Science:

    • Artificial Intelligence in Medicine
    • Biomedical Informatics
    • Clinical Decision Support Systems

    Background:

    • Evidence-based medicine (EBM) relies on rigorous research for patient care.
    • Large language models (LLMs) offer potential for automating EBM tasks and improving efficiency.
    • Integrating LLMs into EBM workflows is a key area for research.

    Purpose of the Study:

    • To explore the integration of LLMs into key stages of EBM.
    • To evaluate LLM performance in evidence retrieval, synthesis, and dissemination.
    • To compare various LLMs and prompting techniques for EBM tasks.

    Main Methods:

    • Comparative analysis of seven LLMs (proprietary, open-source, fine-tuned).
    • Benchmarking LLM performance on PICO extraction, question answering, summarization, and text simplification.
    • Utilizing zero-shot, in-context learning, chain-of-thought, and knowledge-guided prompting.

    Main Results:

    • LLMs demonstrate strong understanding and summarization skills, even in zero-shot settings.
    • Knowledge-guided prompting significantly improved LLM performance on specific tasks (e.g., PICO extraction).
    • LLMs underperformed compared to baselines in named entity recognition and showed factual inconsistencies.

    Conclusions:

    • LLMs show potential for enhancing evidence retrieval, synthesis, and dissemination in EBM.
    • Prompting strategies can improve LLM capabilities, but limitations persist.
    • Rigorous quality control and further research are essential for clinical application of LLMs in EBM.