AI Article Synopsis
- Large language models (LLMs) can help make data extraction faster, cheaper, and with fewer mistakes compared to traditional methods.
- A study found that LLMs are better at understanding and summarizing pathology reports than other language processing techniques.
- However, there are risks like reducing critical thinking, spreading wrong information, and biases, so using rules like CANGARU is important to use LLMs responsibly.
Article Abstract
Large language models (LLMs) have shown promise in reducing time, costs, and errors associated with manual data extraction. A recent study demonstrated that LLMs outperformed natural language processing approaches in abstracting pathology report information. However, challenges include the risks of weakening critical thinking, propagating biases, and hallucinations, which may undermine the scientific method and disseminate inaccurate information. Incorporating suitable guidelines (e.g., CANGARU), should be encouraged to ensure responsible LLM use.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11224331 | PMC |
| http://dx.doi.org/10.1038/s41746-024-01180-y | DOI Listing |
Publication Analysis
Top Keywords
large language
8
language models
8
long road
4
road responsible
4
responsible large
4
models healthcare
4
healthcare large
4
models llms
4
llms promise
4
promise reducing
4
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!