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.