dogear

enter for all results · esc to close

Large language models in healthcare: A comprehensive benchmark

dx.doi.orgsite

a statistical and human evaluation of sixteen different LLMs applied to medical language tasks.

from
Biomedical Information Extraction
added
2026-10-10
likes
0

similar

  1. Assessing the research landscape and clinical utility of large language models: a scoping review doi.org

    a high-level review of LLM applications in medicine as of March 2024.

  2. Ethical and regulatory challenges of large language models in medicine doi.org

    a review of ethical issues arising from applications of LLMs in biomedicine.

  3. Dual retrieving and ranking medical large language model with retrieval augmented generation nature.com

    Scientific Reports, 2025. [All Versions]. Recent advancements in large language models (LLMs) have significantly enhanced text generation across various sectors; however, their medical application faces critical challenges regarding both accuracy and real-time responsiveness…

  4. BioBERT: a pre-trained biomedical language representation model for biomedical text mining academic.oup.com

    Bioinformatics, 2020. [All Versions]. Answering medical questions, identifying relevant clinical trials, and diagnosing diseases based on symptoms, making medical information more accessible to the general public.

  5. BioMedLM huggingface.co

    2.7B parameter GPT-2-style language model trained exclusively on biomedical literature from PubMed for biomedical question answering and text generation.

  6. Large language models for scientific discovery in molecular property prediction nature.com

    Nature Machine Intelligence, 2025. [All Versions]. Large language models (LLMs) are a form of artificial intelligence system encapsulating vast knowledge in the form of natural language. These systems are adept at numerous complex tasks including creative writing, storytelling…

Biomedical Information Extraction › Research Overviews > LLMs in Biomedical IE: “a statistical and human evaluation of sixteen different LLMs applied to medical language tasks.”