Summary of Combining Insights From Multiple Large Language Models Improves Diagnostic Accuracy, by Gioele Barabucci et al.
Combining Insights From Multiple Large Language Models Improves Diagnostic Accuracy
by Gioele Barabucci, Victor Shia, Eugene Chu, Benjamin Harack, Nathan Fu
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 abstract discusses the limitations of large language models (LLMs) in providing accurate diagnostic support. Specifically, it highlights that even LLMs trained on medical topics may not be reliable enough for real-world applications. The study suggests that these models are still far from replacing human doctors and emphasizes the need for further research to improve their diagnostic accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models like GPT-4 and PaLM 2 are being explored as tools for helping diagnose illnesses, but even those specifically trained on medical topics might not be good enough for real-life use. This paper looks at why these models aren’t reliable enough yet and what needs to happen next for them to be useful. |
Keywords
» Artificial intelligence » Gpt » Palm
