The pursuit of trustworthy AI in healthcare—systems that are accurate, auditable, equitable, and secure—has become a paramount goal for hospitals and research institutions worldwide. But as models grow more complex, ingesting multimodal data from genomic sequences to real-time patient monitors, a critical flaw in many initiatives is emerging: a fragmented data foundation. When imaging archives, EHR databases, and lab result systems operate in isolated silos, AI models are trained on incomplete pictures, struggle with data drift, and lack the unified lineage required for clinical validation and regulatory audit. This fragmentation undermines the very trust these systems aim to build. True trustworthiness may be impossible without a cohesive data architecture at its heart. This video argues that Integrated Storage Solutions, which provide a single, performant, and governed data plane for all healthcare data types, are not just an infrastructure upgrade but a prerequisite for ethical and effective AI. We examine how unifying disparate data sources into an intelligent storage core is the essential step towards AI that clinicians can rely on and patients can trust.
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