Summary of Bias Patterns in the Application Of Llms For Clinical Decision Support: a Comprehensive Study, by Raphael Poulain et al.
Bias patterns in the application of LLMs for clinical decision support: A comprehensive study
by Raphael Poulain, Hamed Fayyaz, Rahmatollah Beheshti
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 paper investigates the social biases present in Large Language Models (LLMs) used for clinical decision-making. The authors evaluate eight popular LLMs across three question-answering datasets using standardized clinical vignettes to assess bias. They employ red-teaming strategies to analyze demographics’ impact on model outputs, comparing general-purpose and clinically-trained models. Results reveal significant disparities across protected groups, with larger models not always being less biased and fine-tuned medical data models not necessarily better than general-purpose ones. The study also shows prompt design’s influence on bias patterns and the effectiveness of reflection-type approaches like Chain of Thought in reducing biased outcomes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are powerful tools that can help doctors make decisions, but they might have biases. These biases could be based on things like a patient’s race. The authors of this paper looked at how different models perform when given questions about patients with different characteristics. They used standardized questions and answers to see if the models were biased. The results showed that some models are more biased than others, even though they’re trained on large amounts of data. The study also found that how you ask a question can affect the answer, which is important for making good decisions. |
Keywords
» Artificial intelligence » Prompt » Question answering
