Summary of Can Artificial Intelligence Predict Clinical Trial Outcomes?, by Shuyi Jin et al.
Can artificial intelligence predict clinical trial outcomes?
by Shuyi Jin, Lu Chen, Hongru Ding, Meijie Wang, Lun Yu
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Applications (stat.AP)
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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 study compares the performance of large language models (LLMs) like GPT-4o, GPT-3.5, and Llama3 with the HINT model in predicting clinical trial outcomes. The evaluation metrics used include Balanced Accuracy, Matthews Correlation Coefficient (MCC), Recall, and Specificity. Results show that GPT-4o achieves better overall performance among LLMs but struggles to identify negative outcomes. In contrast, HINT excels in recognizing negative samples and is resilient to external factors like recruitment challenges, but underperforms in oncology trials. The study highlights the strengths of LLMs in early-phase trials and simpler endpoints like Overall Survival (OS), while HINT shows consistency across trial phases and excels in complex endpoints. Additionally, the analysis reveals improved model performance for medium- to long-term trials, with GPT-4o and HINT displaying stability and enhanced specificity respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well big language models do at predicting what will happen in clinical trials. The models are compared using special metrics like accuracy, correlation, and specificity. The results show that one model, called GPT-4o, does better than the others, but it has trouble predicting when something bad happens. A different model, called HINT, is really good at predicting when something bad will happen, but it’s not as good in other areas. This study shows that different models are good at different things, and that using a combination of models could be helpful. |
Keywords
» Artificial intelligence » Gpt » Recall
