Artificial intelligence (AI) has the potential to accelerate research on health disparities by addressing complex social, genetic, and environmental factors that contribute to unequal health outcomes. Health disparities are a pressing issue, influenced by systemic biases and structural inequalities, and AI can help uncover hidden mechanisms and patterns that traditional methods miss. However, AI also presents challenges, including the risk of reinforcing existing biases if not carefully designed and implemented. The authors argue that using AI to address health disparities is a scientific imperative, not just a choice, and emphasize the need for inclusive data, ethical algorithm design, diverse representation in AI development, and a human-centered approach to healthcare. They propose actionable recommendations, such as promoting inclusive data collection, establishing ethical guidelines, ensuring diverse representation in AI teams, and maintaining oversight to prevent AI from exacerbating disparities. The article calls for a collaborative effort to harness AI's potential while addressing its challenges to achieve health equity. The authors stress the importance of balancing AI with human expertise and ensuring that AI is used as a decision support tool rather than a replacement for human interaction. The study highlights the need for ongoing evaluation and improvement of AI models to ensure they remain fair, transparent, and aligned with the goal of reducing health disparities.Artificial intelligence (AI) has the potential to accelerate research on health disparities by addressing complex social, genetic, and environmental factors that contribute to unequal health outcomes. Health disparities are a pressing issue, influenced by systemic biases and structural inequalities, and AI can help uncover hidden mechanisms and patterns that traditional methods miss. However, AI also presents challenges, including the risk of reinforcing existing biases if not carefully designed and implemented. The authors argue that using AI to address health disparities is a scientific imperative, not just a choice, and emphasize the need for inclusive data, ethical algorithm design, diverse representation in AI development, and a human-centered approach to healthcare. They propose actionable recommendations, such as promoting inclusive data collection, establishing ethical guidelines, ensuring diverse representation in AI teams, and maintaining oversight to prevent AI from exacerbating disparities. The article calls for a collaborative effort to harness AI's potential while addressing its challenges to achieve health equity. The authors stress the importance of balancing AI with human expertise and ensuring that AI is used as a decision support tool rather than a replacement for human interaction. The study highlights the need for ongoing evaluation and improvement of AI models to ensure they remain fair, transparent, and aligned with the goal of reducing health disparities.