Summary of Panacea: a Foundation Model For Clinical Trial Search, Summarization, Design, and Recruitment, by Jiacheng Lin et al.
Panacea: A foundation model for clinical trial search, summarization, design, and recruitment
by Jiacheng Lin, Hanwen Xu, Zifeng Wang, Sheng Wang, Jimeng Sun
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Clinical Trial Foundation Model, named Panacea, aims to tackle the limitations of current large language models (LLMs) in clinical trial design and patient-trial matching by developing a single model capable of handling multiple tasks. This includes trial search, summarization, design, and patient-trial matching. To achieve this, the researchers created two large-scale datasets: TrialAlign, comprising 793,279 trial documents and 1,113,207 scientific papers, to infuse clinical knowledge into the model through pre-training; and TrialInstruct, containing 200,866 instruction data for fine-tuning. This enables Panacea to be applied to various clinical trial tasks based on user requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Panacea is a new way to help doctors design better trials for new medicines. Right now, it takes a long time and doesn’t always work out. The researchers want to make it faster and more accurate by creating a special kind of computer program. They made two big collections of information: one with lots of documents about past trials and another with instructions on how to do different tasks related to trials. This will help the program, called Panacea, learn from all this information and be able to help doctors design better trials. |
Keywords
» Artificial intelligence » Fine tuning » Summarization
