Summary of Evidence Is All You Need: Ordering Imaging Studies Via Language Model Alignment with the Acr Appropriateness Criteria, by Michael S. Yao et al.
Evidence Is All You Need: Ordering Imaging Studies via Language Model Alignment with the ACR Appropriateness Criteria
by Michael S. Yao, Allison Chae, Charles E. Kahn Jr., Walter R. Witschey, James C. Gee, Hersh Sagreiya, Osbert Bastani
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computers and Society (cs.CY)
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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 proposed framework leverages large language models to recommend imaging studies for patients, aligning with evidence-based medical guidelines such as the American College of Radiology’s Appropriateness Criteria (ACR AC). The approach optimizes state-of-the-art language models to achieve accuracy on par with clinicians in image ordering. A novel dataset of patient “one-liner” scenarios is made available to power experiments. The framework demonstrates a strategy to leverage AI-based software, improving trustworthy clinical decision making in alignment with expert evidence-based guidelines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses artificial intelligence to help doctors order the right medical images for patients. This can be tricky because there are many options and not all of them are needed. The researchers created a new way to use language models like those used in chatbots, but instead of chatting, they make recommendations about which imaging studies to order based on patient information. They tested this approach using real patient scenarios and found that it worked just as well as doctors do. This can help improve the quality of medical decisions. |
Keywords
» Artificial intelligence » Alignment
