AI AND BIG DATA IN ADDRESSING HEALTHCARE DISPARITIES: FOCUSED STRATEGIES FOR UNDERSERVED POPULATIONS IN THE U.S.

Healthcare disparities in the United States remain a critical challenge, disproportionately affecting underserved populations such as racial and ethnic minorities, rural communities, and low-income groups.These disparities manifest in unequal access to healthcare services, variations in disease prevalence, and poorer health outcomes.The emergence of Artificial Intelligence (AI) and Big Data offers transformative potential to address these systemic inequities by enabling more precise, data-driven interventions.AI algorithms can analyze vast datasets to uncover hidden patterns and correlations, identifying at-risk populations and predicting health outcomes with unprecedented accuracy.Big Data, sourced from electronic health records (EHRs), social determinants of health (SDOH), and real-time health monitoring devices, provides a comprehensive view of patient health and community-level disparities.This paper explores how AI and Big Data can be leveraged to reduce healthcare disparities in the U.S., focusing on developing targeted strategies that improve healthcare access, quality, and outcomes for underserved populations.By integrating predictive analytics, machine learning models, and natural language processing, healthcare providers can better allocate resources, personalize treatment plans, and streamline care coordination.Additionally, AI-driven tools can aid in identifying biases within healthcare systems, ensuring more equitable distribution of care.The paper also discusses challenges such as data privacy, algorithmic bias, and the digital divide, which could exacerbate disparities if not addressed.Solutions include ethical AI frameworks, inclusive data practices, and community engagement to ensure technology adoption benefits all populations equally.Ultimately, AI and Big Data hold the promise to revolutionize healthcare delivery, making it more equitable, efficient, and responsive to the needs of marginalized communities.