The landscape of epidemiological research is experiencing a technological transformation, driven by the rapid expansion of big data and advancements in artificial intelligence (AI) and machine learning (ML). This workshop explored the opportunities and challenges associated with integrating diverse data sources into population-based research at different levels, including electronic health records (EHRs), genomic and omics data, imaging, wearable device data, and social determinants of health measures, among others. AI/ML tools present powerful capabilities for analyzing these vast datasets, offering advancements in health risk prediction, disease pattern identification, and the development of personalized interventions. However, the integration of big data introduces technical barriers related to data heterogeneity, privacy and security concerns, and the potential to exacerbate health disparities through algorithmic biases. In September 2023, the National Institutes of Health's (NIH) National Heart, Lung, and Blood Institute (NHLBI), in collaboration with the National Cancer Institute (NCI) and the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), hosted a workshop to address these challenges and discuss the integration of big data into epidemiology and population-based studies. Key themes from the workshop emphasized interdisciplinary collaboration, data standardization, and the development of robust ethical frameworks, as well as the importance of advancing data governance, implementing transparent consent processes, and employing privacy-preserving techniques to maintain public trust. Additionally, the workshop highlighted the transformative potential of digital health technologies, such as wearable devices, which, when integrated with EHRs, enhance data granularity, facilitate early disease detection, and strengthen public health surveillance. Ethical, legal, and social issues (ELSI) are central to responsibly leveraging big data and AI in research, unbiased algorithms, the use of diverse datasets in AI training, and continuous human oversight to mitigate risk and ensure validity. The workshop also emphasized the need for workforce training and education in data science and bioinformatics to prepare researchers for utilizing these technologies effectively. The workshop concluded by recognizing the need for a balanced approach that addresses data integration challenges while harnessing AI/ML to improve healthcare outcomes. By fostering interdisciplinary collaboration, prioritizing privacy, and embracing data-driven methodologies, epidemiological research can unlock the full potential of big data to transform public health and clinical practice.
Shandhi et al. (Wed,) studied this question.