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From RAGs to riches: Utilizing large language models to write documents for clinical trials

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arxiv 2402.16406 v2 pith:I77AOTH2 submitted 2024-02-26 cs.CL

classification cs.CL
keywords httpslinkpubmedarticlebeenclinicaldocumentsjournal
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This manuscript has now been published: - Link to article on journal website: https://journals.sagepub.com/doi/10.1177/17407745251320806 - Pubmed link: https://pubmed.ncbi.nlm.nih.gov/40013826/

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Towards Automated Situation Awareness: A RAG-Based Framework for Peacebuilding Reports

    cs.CY 2025-05 conditional novelty 4.0 of 10

    A dynamic RAG pipeline generates situation awareness reports for peacebuilding from GDELT, ACLED, ReliefWeb, and World Bank data, evaluated by NLP metrics, UNDP experts, and LLM judges.

  2. Can artificial intelligence predict clinical trial outcomes?

    cs.LG 2024-11 reject novelty 4.0 of 10

    A comparison of LLMs and HINT for clinical trial outcome prediction that is undermined by data leakage and post-hoc exclusions.

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