Summary of Can Llms Correct Physicians, Yet? Investigating Effective Interaction Methods in the Medical Domain, by Burcu Sayin et al.
Can LLMs Correct Physicians, Yet? Investigating Effective Interaction Methods in the Medical Domain
by Burcu Sayin, Pasquale Minervini, Jacopo Staiano, Andrea Passerini
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper investigates the potential of Large Language Models (LLMs) to aid physicians in medical decision-making tasks. The authors evaluate several LLMs, including Meditron, Llama2, and Mistral, to analyze their ability to interact effectively with physicians across different scenarios. They consider various tasks, ranging from binary responses to long answer generation, where the model’s response is produced after an interaction with a physician. The findings suggest that prompt design significantly influences the downstream accuracy of LLMs and that they can provide valuable feedback to physicians, challenging incorrect diagnoses and contributing to more accurate decision-making. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how big computers (Large Language Models) can help doctors make better decisions. Researchers tested different types of these computers, like Meditron, Llama2, and Mistral, to see if they could work well with doctors. They looked at what happens when the computer gives a doctor an answer after talking with them about a medical problem. The results show that how you ask the question is important and that the computer can help doctors make better decisions by pointing out mistakes and giving good answers. |
Keywords
» Artificial intelligence » Prompt
