Evaluating Bias Patterns in Large Language Models for Clinical Decision Support: A Comprehensive Study

Core Concepts

Large language models (LLMs) exhibit varying degrees of social biases when applied to clinical decision support tasks, with some models showing significant disparities across patient demographics. Prompt engineering techniques, such as Chain of Thought, can help mitigate these biases.

Abstract

This study comprehensively evaluates the social biases exhibited by various large language models (LLMs) when applied to clinical decision support tasks. The researchers used three datasets with clinical vignettes (patient descriptions) standardized for bias evaluations, covering pain management, nurse perception, and treatment recommendations.
The key findings are:

Certain LLMs, such as GPT-4, Palmyra-Med, and Meditron, exhibited concerning disparities in their responses based on patient race and gender. For example, Palmyra-Med was more likely to recommend pain medication for Hispanic women compared to other demographics in the chronic cancer task.

Model size (number of parameters) did not necessarily correlate with reduced bias, as both smaller and larger models showed biased patterns.

Prompt engineering techniques, particularly the Chain of Thought (CoT) approach, were found to be effective in reducing biases compared to traditional zero-shot or few-shot prompting. CoT prompting, which encourages LLMs to articulate their reasoning steps, seems to steer the models away from potentially biased shortcuts.

The study highlights the critical need for a multifaceted approach to mitigate bias in LLMs used for clinical decision support. This includes developing prompt engineering techniques, creating diverse and representative training datasets, and fostering transparency and interpretability in these models. Regulatory frameworks and interdisciplinary collaboration are also crucial to ensure the responsible and equitable deployment of LLMs in healthcare.

Stats

"Hispanic women were significantly more likely (p-value ≤0.05) to be recommended pain medication by Palmyra-Med compared to four other groups (Black/Asian/White Man, and White Woman)."
"Meditron, another clinically-tuned model, exhibited biases on three tasks (Chronic Non Cancer, Acute Cancer, and Post Op), with Hispanic women less likely to receive pain medication."
"GPT-4 showed an opposite bias on the Post Op task, favoring Hispanic women for pain medication."

Quotes

"Consistent with prior studies, we call on additional evaluations, scrutiny, and enhancement of LLMs used in clinical decision support applications."
"Prompt engineering techniques, particularly the Chain of Thought (CoT) approach, were found to be effective in reducing biases compared to traditional zero-shot or few-shot prompting."
"The study highlights the critical need for a multifaceted approach to mitigate bias in LLMs used for clinical decision support."

Key Insights Distilled From

Bias patterns in the application of LLMs for clinical decision support: A comprehensive study

by Raphael Poul... at arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.15149.pdf

Deeper Inquiries

How can we ensure that the training data used to develop clinical LLMs is diverse and representative of the patient population?

To ensure that the training data used to develop clinical Large Language Models (LLMs) is diverse and representative of the patient population, several strategies can be implemented:

Data Collection: Collect data from a wide range of sources, including diverse healthcare institutions, regions, and demographics. This can help capture a more comprehensive picture of the patient population.

Data Augmentation: Augment existing datasets to include underrepresented groups by synthesizing additional data points or balancing the distribution of different demographics within the dataset.

Collaboration: Collaborate with healthcare providers, researchers, and community organizations to access a more diverse set of patient data and ensure that the data reflects the real-world patient population.

Ethical Considerations: Ensure that the data collection process adheres to ethical guidelines, including obtaining informed consent, protecting patient privacy, and addressing potential biases in the data collection process.

Regular Evaluation: Continuously evaluate the diversity and representativeness of the training data to identify any gaps or biases and take corrective measures as needed.

Bias Mitigation Techniques: Implement bias mitigation techniques during the training process, such as debiasing algorithms, fairness constraints, and adversarial training, to reduce biases in the model's predictions.

By incorporating these strategies, developers can create clinical LLMs that are trained on diverse and representative data, leading to more equitable and accurate decision support systems in healthcare.

What other types of biases, beyond gender and race, might be present in LLMs used for clinical decision support, and how can we address them?

In addition to gender and race, other types of biases that might be present in Large Language Models (LLMs) used for clinical decision support include:

Socioeconomic Bias: LLMs may exhibit biases based on socioeconomic status, leading to disparities in treatment recommendations and outcomes for patients from different economic backgrounds.

Geographic Bias: LLMs trained on data from specific regions may show biases towards certain geographic locations, impacting the quality of care provided to patients from underrepresented areas.

Language Bias: LLMs may exhibit biases based on language proficiency, potentially affecting the accuracy of diagnoses and treatment recommendations for patients who speak languages other than the dominant language in the training data.

Medical History Bias: Biases related to specific medical conditions, treatments, or healthcare providers present in the training data can influence the model's recommendations and decisions.

To address these biases, developers can:

Diversify Training Data: Include a wide range of socioeconomic backgrounds, geographic locations, languages, and medical histories in the training data to mitigate biases related to these factors.

Regular Bias Audits: Conduct regular audits to identify and address biases in the model's outputs, focusing on various demographic and contextual factors beyond gender and race.

Fairness Constraints: Implement fairness constraints and metrics to ensure that the model's predictions are equitable across different groups and characteristics.

Interpretability: Enhance the interpretability of the model to understand how biases manifest in its decisions and take corrective actions accordingly.
By proactively addressing these biases and implementing mitigation strategies, developers can create more reliable and unbiased clinical decision support systems using LLMs.

How can the healthcare industry and the AI research community collaborate to establish robust regulatory frameworks and guidelines for the responsible deployment of LLMs in clinical settings?

Collaboration between the healthcare industry and the AI research community is essential to establish robust regulatory frameworks and guidelines for the responsible deployment of Large Language Models (LLMs) in clinical settings. Here are some ways they can work together:

Interdisciplinary Task Forces: Form interdisciplinary task forces comprising healthcare professionals, AI researchers, policymakers, ethicists, and patient advocates to develop regulatory frameworks that consider the unique challenges of deploying LLMs in healthcare.

Ethical Guidelines: Collaborate on the development of ethical guidelines specific to the use of LLMs in clinical decision support, addressing issues such as bias, transparency, accountability, and patient privacy.

Data Governance: Establish data governance frameworks that ensure the ethical collection, storage, and use of patient data for training LLMs, with a focus on patient consent, data security, and data sharing protocols.

Transparency and Explainability: Work together to enhance the transparency and explainability of LLMs in healthcare, enabling healthcare providers to understand how the models arrive at their decisions and ensuring accountability for the outcomes.

Regulatory Compliance: Align on regulatory compliance requirements for LLMs in healthcare, ensuring that the models meet industry standards, legal obligations, and patient safety regulations.

Continuous Monitoring: Collaborate on establishing mechanisms for continuous monitoring and evaluation of LLMs in clinical settings to detect and address any biases, errors, or adverse effects that may arise during deployment.

By fostering collaboration between the healthcare industry and the AI research community, stakeholders can create a regulatory environment that promotes the responsible and ethical use of LLMs in clinical decision support, ultimately enhancing patient care and outcomes.