Summary of Can Large Language Models Logically Predict Myocardial Infarction? Evaluation Based on Uk Biobank Cohort, by Yuxing Zhi et al.
Can Large Language Models Logically Predict Myocardial Infarction? Evaluation based on UK Biobank Cohort
by Yuxing Zhi, Yuan Guo, Kai Yuan, Hesong Wang, Heng Xu, Haina Yao, Albert C Yang, Guangrui Huang, Yuping Duan
First submitted to arxiv on: 22 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models (LLMs) have made significant advancements in clinical decision support, but high-quality evidence is needed on their potential and limitations. This study aimed to evaluate whether universal state-of-the-art LLMs, such as ChatGPT and GPT-4, can predict the incidence risk of myocardial infarction (MI) with logical inference. The study used a retrospective cohort from the UK Biobank database, transforming risk factor data into standardized textual descriptions for ChatGPT recognition. Responses were generated by asking ChatGPT to select a score representing the risk. Chain of Thought questioning was used to evaluate whether LLMs make prediction logically. The predictive performance of ChatGPT was compared with published medical indices and traditional machine learning models. However, the study concluded that current LLMs are not yet ready for clinical medicine applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special computer programs called large language models (LLMs) to help doctors make better decisions. Right now, these models are very good at doing things like answering questions and writing text, but they’re not very good at understanding medical information and making smart decisions based on that information. The researchers used a big database of health information from the UK Biobank to test how well these LLMs could predict when someone might have a heart attack. They found that while the models were good at recognizing certain patterns, they didn’t do very well overall. This means that we still need to work on developing better models that can understand both medical language and numbers. |
Keywords
» Artificial intelligence » Gpt » Inference » Machine learning
