Summary of Stochastic Parrots or Icu Experts? Large Language Models in Critical Care Medicine: a Scoping Review, by Tongyue Shi et al.
Stochastic Parrots or ICU Experts? Large Language Models in Critical Care Medicine: A Scoping Review
by Tongyue Shi, Jun Ma, Zihan Yu, Haowei Xu, Minqi Xiong, Meirong Xiao, Yilin Li, Huiying Zhao, Guilan Kong
First submitted to arxiv on: 27 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 Machine learning educators can summarize this abstract by stating that a scoping review aimed to explore the application of large language models (LLMs) in critical care medicine (CCM). The study searched through seven databases, including PubMed and Scopus, and selected 24 articles from an initial pool of 619. The applications of LLMs in CCM were categorized into three areas: clinical decision support, medical documentation and reporting, and medical education and doctor-patient communication. While LLMs have advantages such as handling unstructured data without manual feature engineering, they also face challenges like hallucinations, poor interpretability, bias, alignment challenges, and privacy and ethics issues. The study emphasizes the need for future research to enhance model reliability and interpretability, integrate up-to-date medical knowledge, and strengthen privacy and ethical guidelines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are being used to help doctors make better decisions in critical care medicine. A recent review looked at how LLMs can be applied in this field. The reviewers searched through many articles and found that LLMs can be helpful in making diagnoses, documenting patient information, and teaching medical students. However, there are some challenges to using LLMs, such as them sometimes making mistakes or not being very clear about why they made a certain decision. To make sure LLMs are used safely and effectively, researchers need to keep working on improving their performance and making sure they follow proper guidelines. |
Keywords
» Artificial intelligence » Alignment » Feature engineering » Machine learning
