Summary of Can Rule-based Insights Enhance Llms For Radiology Report Classification? Introducing the Radprompt Methodology, by Panagiotis Fytas et al.
Can Rule-Based Insights Enhance LLMs for Radiology Report Classification? Introducing the RadPrompt Methodology
by Panagiotis Fytas, Anna Breger, Ian Selby, Simon Baker, Shahab Shahipasand, Anna Korhonen
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 authors propose RadPert, a rule-based system for extracting labels from radiology reports to supervise imaging models in detecting pathologies from chest X-rays. The approach integrates an uncertainty-aware information schema with a streamlined set of rules, enhancing performance and robustness. Additionally, the authors develop RadPrompt, a multi-turn prompting strategy that leverages RadPert to improve the zero-shot predictive capabilities of large language models. This synergy enables RadPrompt to surpass both its underlying models, achieving a statistically significant improvement in weighted average F1 score over GPT-4 Turbo. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a way to use text reports from doctors to help train machines to recognize problems on chest X-rays. They made two tools: RadPert and RadPrompt. RadPert is a system that uses rules to understand doctor’s reports and make them useful for training machines. RadPrompt is a way to ask large language models questions to help them get better at predicting what’s in an X-ray. This combination of old and new technology makes the predictions much better. |
Keywords
* Artificial intelligence * F1 score * Gpt * Prompting * Zero shot
