Machine learning algorithms used in healthcare may contain sociodemographic biases that contribute to health disparities. These biases can arise from training data that reflects societal inequities, such as race, gender, age, and socioeconomic status. Biases in machine learning models can lead to unfair healthcare outcomes, especially for marginalized populations. The authors highlight various types of biases, including implicit bias, selection bias, and cultural bias, and discuss their impact on healthcare decision-making. They also propose strategies to mitigate these biases, such as using de-biasing techniques during training, ensuring diverse and representative training data, and incorporating fairness into model design. The paper emphasizes the need for transparency, fairness, and accountability in healthcare AI systems. It calls for a community-driven approach to developing fair and accurate AI models that reflect the diversity of the population. The authors argue that addressing these biases is essential to ensure equitable healthcare outcomes and reduce disparities. They also discuss the challenges of using large language models in healthcare, including the risk of perpetuating biases and the need for ongoing monitoring and adjustment. The paper concludes with recommendations for improving healthcare AI, including the use of diverse training data, the implementation of fairness-aware algorithms, and the inclusion of diverse perspectives in model development.Machine learning algorithms used in healthcare may contain sociodemographic biases that contribute to health disparities. These biases can arise from training data that reflects societal inequities, such as race, gender, age, and socioeconomic status. Biases in machine learning models can lead to unfair healthcare outcomes, especially for marginalized populations. The authors highlight various types of biases, including implicit bias, selection bias, and cultural bias, and discuss their impact on healthcare decision-making. They also propose strategies to mitigate these biases, such as using de-biasing techniques during training, ensuring diverse and representative training data, and incorporating fairness into model design. The paper emphasizes the need for transparency, fairness, and accountability in healthcare AI systems. It calls for a community-driven approach to developing fair and accurate AI models that reflect the diversity of the population. The authors argue that addressing these biases is essential to ensure equitable healthcare outcomes and reduce disparities. They also discuss the challenges of using large language models in healthcare, including the risk of perpetuating biases and the need for ongoing monitoring and adjustment. The paper concludes with recommendations for improving healthcare AI, including the use of diverse training data, the implementation of fairness-aware algorithms, and the inclusion of diverse perspectives in model development.