Summary of Softtiger: a Clinical Foundation Model For Healthcare Workflows, by Ye Chen et al.
SoftTiger: A Clinical Foundation Model for Healthcare Workflows
by Ye Chen, Igor Couto, Wei Cai, Cong Fu, Bruno Dorneles
First submitted to arxiv on: 1 Mar 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 paper introduces SoftTiger, a clinical large language model (CLaM) designed as a foundation model for healthcare workflows. The goal is to address the challenge of structuring clinical notes into clinical data according to international interoperability standards. The authors collect and annotate data for three subtasks: international patient summary, clinical impression, and medical encounter. They fine-tune a state-of-the-art LLM using public and credentialed clinical data, targeting basic clinical tasks like abbreviation expansion and temporal information extraction, as well as more complex downstream clinical tasks. The training is designed to support extra long context windows in the healthcare context. The authors compare SoftTiger’s performance with other popular open-source models and GPT-3.5, showing it outperforms them, with a mild gap from GPT-4. They believe that LLMs can become a step-stone towards healthcare digitalization and democratization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a tool called SoftTiger to help doctors use computers better. It’s like a super smart dictionary for medical notes. The problem is that these notes are often hard to read because they’re written in a special way. The authors fixed this by creating a system that can understand and organize the notes correctly. They used real medical data to train the system, so it can learn to do things like expand abbreviations and find important information. This tool could help doctors work more efficiently and make healthcare better. |
Keywords
» Artificial intelligence » Gpt » Large language model
