A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document-Reference-Cited by-同舟云学术

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A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document

Published:2024-05 Issue: Volume:149 Page:105613
ISSN:0273-2300
Container-title:Regulatory Toxicology and Pharmacology
language:en
Short-container-title:Regulatory Toxicology and Pharmacology

Author:

Wu Leihong^ORCID,Xu Joshua,Thakkar Shraddha,Gray Magnus,Qu Yanyan,Li Dongying,Tong Weida

Funder

US FDA

Publisher

Elsevier BV

Reference27 articles.

1. Chu, N., 2023. Amazon falcon lite vs OpenAI ChatGPT - the Large Language model battle. HackerNoon. https://hackernoon.com/amazon-falcon-lite-vs-openai-chatgpt-the-large-language-model-battle. (Accessed 25 August 2023).

2. FDALabel: AMIKACIN SULFATE. 2023. https://nctr-crs.fda.gov/fdalabel/services/spl/set-ids/7791d3e1-9287-4f75-b4f0-e5d35cacfd6e/spl-doc (accessed August/22/2023).

3. FDALabel for drug repurposing studies and beyond;Fang;Nat. Biotechnol.,2020

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. UniTox: Leveraging LLMs to Curate a Unified Dataset of Drug-Induced Toxicity from FDA Labels;2024-06-22

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