Artificial Intelligence （AI） promises to revolutionize patient care， medical research， and help improve health outcomes， yet it raises critical Will AI help eliminate deep-rooted health disparities， or will it exacerbate them？ "Algorithmic Removing Biases and Improving Data for AI in Healthcare" explores this pivotal issue， offering a comprehensive look at how AI can be both a powerful ally and a potential adversary in our quest for health equity。 The book examines AI's ability to analyze growing volumes of genetic and medical data with unprecedented speed and precision， which could revolutionize personalized medicine and improve health outcomes。 However， it also highlights the risks of perpetuating existing disparities， arguing that without careful design， AI could reinforce biases， leading to unequal care and poorer outcomes for marginalized communities。 Ethical， technical， and societal challenges such as data privacy， representative datasets， and community involvement in AI development are addressed， making a compelling case for robust policy frameworks to ensure AI bridges， rather than widens， the gap in health equity。 Ultimately， Algorithmic Inequality offers a vision of a future where AI enhances health outcomes for all， particularly those historically underserved by healthcare systems， and calls for collective action to realize AI's potential in a way that benefits everyone。 This book is essential for healthcare professionals， policymakers， AI developers， patients and caregivers or anyone interested in the intersection of healthcare technology and social justice， serving as both a cautionary tale and a call to action。