Enhancing Medical Research Data Exchange Using Cloud Services with Hybrid Contrastive Language-Image Pretraining and Bayesian Optimization

The exponential growth of healthcare data aided by the work of cloud computing, Artificial intelligence (AI), and the Internet of Things (IoT), security, scalability, and processing efficiency are quite relevant challenges. The existing approaches suffer from fragmented data storage, weak security protocol specifications, and a lack of interoperability. They, thus, end up being inefficient in integrating multi-modal data, leaving out useful information for decision-making. Moreover, data encryption security is not strong enough, thus compromising data privacy issues and regulatory compliance. Our proposed framework based on the fusion of clinical text and medical images aims to advance the decision-making and predictive accuracy of this process. Data are pre-processed through normalization and imputation methods so that consistency and completeness are guaranteed. Data privacy and compliance with the privacy regulation of HIPAA and GDPR are maintained via secure cloud-storage architecture, role-based access control (RBAC), and encryption mechanisms. The Bayesian optimization approach in fine-tuning the CLIP model would gain performance with minimal evaluations. Experimental results demonstrate how effective this model is with accuracies of 98%, 98% precision, 97% recall, and 97% F1-score. The scalability study also proved that large data sets could be accommodated by the model, showing great promise for data security and efficient exchange of stored medical data within the cloud. These results give evidence for the hope that the CLIP-BO has to change the landscape of health care data management in collaboration for research and personal care while tackling data security and privacy issues.