Scoping Review of Artificial Intelligence Applications in Global Burden of Disease Studies: Current Practices, Conceptual Models, and Ethical Challenges

Abstract

The integration of artificial intelligence (AI) into Global Burden of Disease (GBD) studies presents transformative opportunities to enhance data collection, analysis, and dissemination. This scoping review synthesizes current applications, conceptual models, and ethical challenges of AI in GBD workflows, guided by PRISMA-ScR methodology. Findings from nine key studies reveal AI's role in automating data integration from diverse sources—such as unstructured text, geospatial datasets, and electronic health records—using techniques like natural language processing (NLP) and multimodal deep learning. AI also advances predictive modeling, risk factor estimation, and real-time disease forecasting, improving the granularity and scalability of burden metrics. In dissemination, AI-powered tools like ChatGPT and interactive dashboards facilitate personalized and accessible reporting for stakeholders. However, challenges persist, including data disparities, algorithmic bias, and ethical concerns, particularly in low-resource settings. The review highlights the need for operational validation, standardized protocols, and equitable AI deployment to ensure these technologies enhance global health equity. Future research should prioritize real-world implementations, bias mitigation, and interdisciplinary collaboration to fully harness AI's potential in GBD studies.