Introduction

The latest research in artificial intelligence (AI) and machine learning (ML) has completely transformed healthcare industry, providing solutions for risk prediction, disease diagnosis, and outcome forecasting. The integration of AI/ML with real-world data (RWD) has shown promise in improving healthcare decision-making processes. However, concerns about algorithmic bias and fairness have emerged, emphasising the need for comprehensive research in this area.

Understanding Algorithmic Bias in Healthcare

Algorithmic fairness in AI/ML applications is crucial to prevent biases that could disproportionately impact different societal groups. Furthermore, examples from healthcare, such as biassed health cost predictions and disparities in disease outcomes, underscore the importance of fair AI/ML practices in healthcare settings.

Assessing Fairness in AI/ML Models

Researchers use metrics such as equality of opportunity, predictive parity, and statistical parity to assess fairness in ML models. Subsequently, they commonly apply pre-processing techniques like reweighing and data imputation to mitigate bias and improve fairness in healthcare applications.

Mitigating Bias in Healthcare AI/ML

Studies have explored pre-processing, in-processing, and post-processing methods to address bias in ML models. Furthermore, techniques such as recalibration and reweighing have shown promise in improving fairness and reducing disparities in healthcare predictions.

Future Research and Recommendations

Future research should focus on expanding fair ML practices into multi-modality and unstructured data. Consequently, this enhances model interpretability, addressing biases in data collection and governance. Collaborative efforts among AI experts, healthcare professionals, and ethicists are essential to ensure the ethical and equitable use of AI/ML in healthcare settings.

Advancing fair AI/ML practices in healthcare with RWD highlights the need for ongoing research. This promotes trustworthy and inclusive healthcare decision-making processes. Continuous exploration in this field is crucial. Lastly, it highlights the critical nature of ongoing investigations in advancing healthcare AI/ML practices.