Detecting Bias in Medical Curriculum Content: A Framework Leveraging Word Sense Disambiguation and Language Models

Keskeiset käsitteet

Biased medical curricula can perpetuate health disparities, so this work proposes a framework to detect such bias using natural language processing models, including the use of Word Sense Disambiguation to improve the quality of training data.

Tiivistelmä

This paper presents a framework for detecting bias in medical curriculum content using natural language processing models. The key highlights are:

1. The authors build on previous work that introduced the BRICC dataset, which contains medical instructional materials annotated by experts for bias. The dataset includes both labeled positive (biased) and negative (non-biased) samples.

2. To improve the quality of the negative samples, the authors propose using Word Sense Disambiguation (WSD) models to filter out irrelevant sentences that contain social identifier terms but are not actually related to the target bias categories (e.g., race, ethnicity, etc.).

3. The authors evaluate various Transformer-based models, including fine-tuned versions of BERT, RoBERTa, and BioBERT, as well as prompting Large Language Models (LLMs) like GPT-4o mini for the bias detection task.

4. The results show that fine-tuned BERT models, particularly RoBERTa, outperform the LLMs and achieve performance comparable to a previous baseline, with the added benefit of improved precision through the use of WSD-filtered negative samples.

5. The authors also discuss future directions, such as further enhancing the synthetic data generation process and exploring the use of WSD for other social identifier categories beyond race and ethnicity.

Overall, this work presents a comprehensive framework for detecting bias in medical curriculum content, highlighting the importance of data quality and the effective use of natural language processing techniques.

Tilastot

"5 Year Relative Survival: overall 84% for white women, 62% for black women, 95% for local disease, 69% regional disease (spread to lymph node), 17% for distant disease."
"Recent meta-analysis suggested no difference in prevalence among countries, rate is 1-2% with increase during late adolescence."

Lainaukset

"There have been growing concerns around high-stake applications that rely on models trained with biased data, which consequently produce biased predictions, often harming the most vulnerable."
"By harnessing machine learning to analyze and detect these biases, we can advance equity in medical training and the fairness of AI models, leading to a more accurate and effective healthcare system."
"Inspired by this framework, we tackle bias detection in medical curricula using NLP models, including LLMs, and evaluate them on a gold standard dataset containing 4,105 excerpts annotated by medical experts for bias from a large corpus."

Tärkeimmät oivallukset

Towards Fairer Health Recommendations: finding informative unbiased samples via Word Sense Disambiguation

by Gavin Butts,... klo arxiv.org 09-12-2024

https://arxiv.org/pdf/2409.07424.pdf

Syvällisempiä Kysymyksiä

How can the proposed framework be extended to detect bias in other types of medical content beyond curriculum, such as clinical notes or research publications?

The proposed framework for detecting bias in medical curricula can be effectively adapted to other types of medical content, such as clinical notes and research publications, by leveraging its core components: bias detection models, Word Sense Disambiguation (WSD), and the systematic approach to data refinement.

Data Collection and Annotation: Similar to the BRICC dataset, new datasets can be created from clinical notes and research publications. These datasets should be annotated by medical experts to identify instances of bias related to social identifiers such as race, gender, and age. The annotation process can be tailored to the specific context of clinical notes, which may include biases in treatment recommendations or diagnostic language, and research publications, which may reflect biases in study design or reporting.

WSD Application: The WSD models can be employed to filter out irrelevant or ambiguous terms in clinical notes and research articles. For instance, terms that may have multiple meanings in a clinical context (e.g., "black" in "black box" versus "black" as a racial identifier) can be disambiguated to ensure that only relevant instances of bias are flagged.

Model Fine-Tuning: The bias detection models, such as fine-tuned BERT variants, can be retrained on the newly annotated datasets. This retraining will help the models learn the specific nuances and contexts of bias present in clinical notes and research publications, enhancing their accuracy and effectiveness.

Evaluation Metrics: The evaluation framework can be adapted to include metrics that are particularly relevant to clinical and research contexts, such as the impact of identified biases on patient outcomes or the integrity of research findings.

Integration with Clinical Decision Support Systems: The framework can be integrated into clinical decision support systems to provide real-time bias detection in clinical notes, helping healthcare professionals make more equitable decisions.

By extending the framework in these ways, it can serve as a robust tool for identifying and mitigating bias across various types of medical content, ultimately contributing to more equitable healthcare practices.

What are the potential limitations of using Word Sense Disambiguation for bias detection, and how can these be addressed?

While Word Sense Disambiguation (WSD) is a powerful tool for improving the quality of data used in bias detection, it does have several limitations that need to be addressed:

Ambiguity in Context: WSD relies heavily on context to determine the correct meaning of a word. In medical texts, the context can be complex and nuanced, leading to potential misclassification. For example, the term "white" could refer to race or to anatomical structures (e.g., "white matter"). To address this, a more sophisticated context analysis could be implemented, possibly using contextual embeddings from advanced language models that capture the surrounding text more effectively.

Limited Training Data: The effectiveness of WSD models is contingent on the availability of high-quality annotated training data. In the medical domain, such data may be scarce or biased itself. To mitigate this, researchers can employ data augmentation techniques, such as generating synthetic examples using language models like ChatGPT, to enhance the training dataset.

Domain-Specific Challenges: Medical terminology often includes jargon and abbreviations that may not be well-represented in general WSD models. Developing domain-specific WSD models trained on medical corpora can help improve accuracy. Collaborating with medical professionals to refine the model's understanding of context-specific terms can also enhance performance.

Computational Complexity: Implementing WSD can introduce additional computational overhead, especially when processing large datasets. Optimizing the WSD algorithms for efficiency and exploring parallel processing techniques can help alleviate this issue.

Evaluation of WSD Performance: The performance of WSD models needs to be rigorously evaluated to ensure they are effectively improving bias detection. Establishing clear benchmarks and conducting comparative studies with and without WSD can provide insights into its impact.

By addressing these limitations, the application of WSD in bias detection can be significantly enhanced, leading to more accurate identification of biases in medical texts.

How can the insights from this work on bias in medical education be applied to improve the fairness and inclusivity of healthcare systems more broadly?

The insights gained from the research on bias in medical education can be instrumental in fostering fairness and inclusivity within healthcare systems in several ways:

Curriculum Reform: The findings highlight the need for medical curricula to be critically evaluated and reformed to eliminate biases. By incorporating training on cultural competence, implicit bias, and the social determinants of health, future healthcare professionals can be better equipped to provide equitable care to diverse populations.

Bias Detection in Clinical Practice: The methodologies developed for bias detection in educational materials can be adapted for use in clinical settings. Implementing similar frameworks to analyze clinical notes, treatment protocols, and patient interactions can help identify and mitigate biases that affect patient care.

Policy Development: Insights from the research can inform healthcare policies aimed at reducing disparities. Policymakers can utilize the findings to create guidelines that promote equitable treatment practices and ensure that healthcare systems are held accountable for addressing biases.

Training and Continuous Education: Ongoing training programs for healthcare professionals can be established to raise awareness about biases and their impact on patient outcomes. Incorporating findings from bias detection studies into continuing medical education can help practitioners remain vigilant against biases in their practice.

Patient Engagement: Engaging patients in discussions about bias and inclusivity can empower them to advocate for their own care. Healthcare systems can develop resources and support networks that encourage patients to voice concerns about potential biases in their treatment.

Research and Data Collection: The research emphasizes the importance of collecting and analyzing data on health outcomes across different demographics. By ensuring that research studies are inclusive and representative, healthcare systems can better understand and address disparities in care.

By applying these insights, healthcare systems can work towards creating a more equitable environment that acknowledges and addresses biases, ultimately leading to improved health outcomes for all patients.