Summary of Benchmarking Llms and Slms For Patient Reported Outcomes, by Matteo Marengo et al.
Benchmarking LLMs and SLMs for patient reported outcomes
by Matteo Marengo, Jarod Lévy, Jean-Emmanuel Bibault
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
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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 study explores the application of Small Language Models (SLMs) in summarizing patient-reported outcomes (PROs) for clinicians. Building upon the success of Large Language Models (LLMs) like GPT-4, SLMs offer the advantage of being deployable locally, ensuring patient data privacy and compliance with healthcare regulations. The paper benchmarks several SLMs against LLMs for summarizing patient-reported Q&A forms in radiotherapy contexts. Using various metrics, the study evaluates their precision and reliability, highlighting both the promise and limitations of SLMs for high-stakes medical tasks. This research has implications for developing more efficient and privacy-preserving AI-driven healthcare solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at using special language models to help doctors summarize what patients have told them about their treatment experiences. Right now, big language models like GPT-4 are good at doing this, but small language models could be even better because they can work on patient data right where the doctor is, keeping everything private and secure. The study compares these two types of models to see how well they do at summarizing patient questions and answers about radiotherapy treatment. By looking at how accurate and reliable each model is, this research can help make healthcare more efficient and safe. |
Keywords
» Artificial intelligence » Gpt » Precision
