AI IMPLEMENTATION AND INTEGRATION
Purpose: To address the practical aspects of incorporating AI into existing... View more
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“Overcoming Data Silos in AI Implementation”
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“Overcoming Data Silos in AI Implementation”
Hello everyone. As we work to implement AI solutions across our healthcare system, one of the biggest challenges we’re facing is data silos. How are others addressing this issue to ensure AI models have access to comprehensive, high-quality data?
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This discussion was modified 1 month, 3 weeks ago by
Maria.
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This discussion was modified 1 month, 3 weeks ago by
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3 Replies
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Great topic, Maria. We’ve been tackling this through a federated learning approach. It allows us to train AI models across multiple decentralized data sources without actually moving the data. It’s been particularly useful for maintaining patient privacy while still leveraging diverse datasets.
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We’ve had success with implementing a data lake architecture. It allows us to store structured and unstructured data from various sources in a centralized repository. The challenge has been ensuring proper data governance and quality control.
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From a clinical perspective, we’ve found that involving clinicians in the data integration process is crucial. They can provide valuable insights on data relevance and quality that might not be apparent to IT teams.
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