Summary of Large Language Models in Healthcare and Medical Domain: a Review, by Zabir Al Nazi et al.
Large language models in healthcare and medical domain: A review
by Zabir Al Nazi, Wei Peng
First submitted to arxiv on: 12 Dec 2023
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 This paper surveys large language models (LLMs) designed for healthcare applications, exploring their potential to amplify efficiency and effectiveness. The survey covers a range of clinical language understanding tasks, including named entity recognition, relation extraction, natural language inference, multi-modal medical applications, document classification, and question-answering. It also compares state-of-the-art LLMs in the healthcare domain, assesses open-source LLMs, and highlights their significance. The paper presents essential performance metrics for evaluating LLMs in biomedicine, discussing effectiveness and limitations. The survey concludes by summarizing prominent challenges and constraints faced by LLMs in healthcare, offering a holistic perspective on their potential benefits and shortcomings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how big language models can help the healthcare system. These models are really good at understanding medical knowledge and answering questions. They can be used for many tasks like finding important information in patient records or helping doctors answer tricky questions. The paper compares different models to see which ones work best and talks about what they’re good at and what they’re not so good at. It also highlights the challenges of using these models in healthcare, but overall it shows how big language models can make a big difference. |
Keywords
* Artificial intelligence * Classification * Inference * Language understanding * Multi modal * Named entity recognition * Question answering
