The growing complexity of clinical research demands effective integration of diverse data modalities, including electronic health records, medical imaging, genomic sequences, and wearable sensor data. Traditional data management approaches often struggle to ensure interoperability, scalability, and real-time processing. This study proposes an AI-enhanced data integration framework tailored for multimodal clinical research. By leveraging machine learning, natural language processing, and advanced data harmonization techniques, the framework enables seamless fusion of structured and unstructured data across heterogeneous sources. The proposed architecture enhances data quality, improves analytical efficiency, and supports reproducibility in clinical studies. Case applications demonstrate its potential to accelerate disease modeling, personalized treatment strategies, and predictive analytics, while maintaining compliance with healthcare data governance standards. Findings suggest that AI-driven integration not only optimizes multimodal research workflows but also paves the way for more holistic and evidence-based clinical decision-making.
Chinthalapelly et al. (Tue,) studied this question.