Social Determinants of Health and AI: Integrating Data for Community-Based Interventions

Аннотация

The integration of Artificial Intelligence (AI) into healthcare is revolutionizing how Social Determinants of Health (SDOH) are addressed in community-based interventions. SDOH—including factors such as income, education, housing, and access to nutritious food—play a pivotal role in shaping health outcomes. By leveraging AI-driven analytics, vast datasets from electronic health records, census data, social services, and environmental sources can be synthesized to identify at-risk populations and predict health disparities. Machine learning algorithms and natural language processing tools enhance the capacity to analyze unstructured data, extract meaningful patterns, and support real-time decision-making. These insights empower public health professionals and policymakers to design targeted, culturally relevant interventions tailored to specific community needs. Furthermore, AI facilitates dynamic monitoring and evaluation of programs, enabling continuous improvement and resource optimization. However, ethical concerns related to data privacy, algorithmic bias, and equitable access to technology must be systematically addressed to ensure fair implementation. Interdisciplinary collaboration among technologists, healthcare providers, and community stakeholders is critical to translating AI insights into actionable strategies that uphold equity and trust. As the digital health landscape evolves, integrating SDOH into AI models offers a transformative path toward more proactive, inclusive, and sustainable health interventions that resonate with real-world community challenges.