Summary of A Preliminary Study Of O1 in Medicine: Are We Closer to An Ai Doctor?, by Yunfei Xie et al.
A Preliminary Study of o1 in Medicine: Are We Closer to an AI Doctor?
by Yunfei Xie, Juncheng Wu, Haoqin Tu, Siwei Yang, Bingchen Zhao, Yongshuo Zong, Qiao Jin, Cihang Xie, Yuyin Zhou
First submitted to arxiv on: 23 Sep 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 A new language model, OpenAI’s o1, has been trained using reinforcement learning strategies and a chain-of-thought technique. This paper evaluates the performance of o1 on various medical scenarios, including understanding, reasoning, and multilinguality tasks. The evaluation uses 37 medical datasets, including two newly constructed question-answering (QA) tasks based on professional medical quizzes from the New England Journal of Medicine (NEJM) and The Lancet. Results show that o1 surpasses GPT-4 in accuracy by an average of 6.2% and 6.6% across 19 datasets and two complex QA scenarios. However, the paper also identifies weaknesses in the model’s capability and existing evaluation protocols, including hallucination, inconsistent multilingual ability, and discrepant metrics for evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at a new language model called o1 that can understand and reason about medical information. The model was tested on different medical scenarios to see how well it did. It used 37 datasets, which are collections of medical data, including two new ones that were made just for this study. The results show that o1 is better than another model called GPT-4 at answering questions and making decisions about medical information. |
Keywords
» Artificial intelligence » Gpt » Hallucination » Language model » Question answering » Reinforcement learning
