Summary of Evaluating the Impact Of Lab Test Results on Large Language Models Generated Differential Diagnoses From Clinical Case Vignettes, by Balu Bhasuran et al.
Evaluating the Impact of Lab Test Results on Large Language Models Generated Differential Diagnoses from Clinical Case Vignettes
by Balu Bhasuran, Qiao Jin, Yuzhang Xie, Carl Yang, Karim Hanna, Jennifer Costa, Cindy Shavor, Zhiyong Lu, Zhe He
First submitted to arxiv on: 1 Nov 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 In this study, researchers examine how lab test results influence the diagnostic abilities of large language models (LLMs) in medicine. They create clinical vignettes based on real-world case reports from PubMed Central and use five different LLMs to generate potential diagnoses with and without lab data. The results show that GPT-4 outperforms other models, achieving 55% accuracy for the top diagnosis when lab results are included, and 60% for the top 10 diagnoses. Lab tests significantly improve diagnostic accuracy, with GPT-4 and Mixtral performing particularly well. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses artificial intelligence to help doctors diagnose medical conditions more accurately. Researchers test how well large language models can figure out what’s wrong with a patient based on their symptoms and lab test results. They find that one model, called GPT-4, is especially good at this task. With the help of lab tests, GPT-4 can make accurate diagnoses about 60% of the time. |
Keywords
» Artificial intelligence » Gpt
