AI-Driven Healthcare Governance and Software Testing Framework for Cloud-Based Medical Systems using Multimodal BERT and Imaging Augmentation
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806806Keywords:
AI-Driven Testing, Healthcare Cloud Systems, Multimodal BERT, Medical Imaging, Data Augmentation, Software Governance, Explainability, Compliance, BioBERT, Databricks AIAbstract
The integration of artificial intelligence (AI) into healthcare demands robust governance and validation mechanisms to ensure security, reliability, and ethical compliance across digital medical systems. This paper proposes an AI-Driven Healthcare Governance and Software Testing Framework for cloud-based medical infrastructures, leveraging Multimodal BERT and medical imaging augmentation to enhance automation, accuracy, and trust in healthcare applications. The framework combines natural language understanding, visual feature extraction, and predictive analytics to test and validate cloud-hosted clinical applications and electronic health systems. Using Multimodal BERT, the system interprets complex medical text, diagnostic images, and metadata for integrated validation workflows, while imaging augmentation improves model generalization and defect detection. Governance modules enforce data privacy, compliance, and continuous monitoring aligned with standards such as HIPAA and GDPR. Experimental evaluations show enhanced test coverage, fault detection rates, and governance traceability compared to traditional testing approaches. This research contributes to developing secure, interpretable, and governance-aware AI ecosystems, ensuring the reliability of cloud-based healthcare software through multimodal intelligence and ethical automation.
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