Summary of A Large Language Model Pipeline For Breast Cancer Oncology, by Tristen Pool and Dennis Trujillo
A Large Language Model Pipeline for Breast Cancer Oncology
by Tristen Pool, Dennis Trujillo
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 abstract discusses how large language models (LLMs) can be developed for oncology, specifically for breast cancer treatment. Researchers fine-tuned OpenAI models on clinical datasets and guidelines text using a novel prompt engineering pipeline. The results showed high accuracy in classifying adjuvant radiation therapy and chemotherapy for breast cancer patients, with an estimated 8.2% to 13.3% of scenarios where the model outperforms human oncologists. While future investigation is needed to determine if this threshold is met, the study highlights the potential of LLMs in expanding access to quality care. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research explores how large language models can be used for breast cancer treatment decisions. The goal was to fine-tune OpenAI models on clinical data and guidelines to improve their accuracy. The results were promising, showing high accuracy in classifying treatment options. While more work is needed, the study suggests that these models could help make quality care more accessible. |
Keywords
» Artificial intelligence » Prompt
