Summary of Can Large Language Models Replace Data Scientists in Clinical Research?, by Zifeng Wang et al.
Can Large Language Models Replace Data Scientists in Clinical Research?
by Zifeng Wang, Benjamin Danek, Ziwei Yang, Zheng Chen, Jimeng Sun
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Genomics (q-bio.GN); Quantitative Methods (q-bio.QM)
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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 The paper explores the capabilities of large language models (LLMs) in handling medical data science tasks and their practical utility in clinical research. To assess this, a dataset was created consisting of real-world coding tasks based on published clinical studies. The results show that cutting-edge LLMs struggle to generate perfect solutions, often failing to follow instructions or understand target data. Advanced adaptation methods were benchmarked, revealing two effective approaches: chain-of-thought prompting and self-reflection. A platform was developed integrating LLMs into the data science workflow for medical professionals, improving code accuracy and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well large language models (LLMs) can do tasks that need coding in medicine. To test this, a big dataset was made with real-world coding tasks based on medical studies. The results show that LLMs are not good enough yet to fully automate these tasks. They often get things wrong or don’t understand the data they’re working with. Some new ways of using LLMs were tested and two of them worked well: giving them a step-by-step plan and letting them try again. |
Keywords
» Artificial intelligence » Prompting
