High-quality data is the foundation of effective AI in healthcare. This whitepaper stresses that poor data leads to biased, inaccurate, and unsafe outcomes, reinforcing the principle of “garbage in, garbage out.” It outlines key factors—volume, velocity, variety, veracity, validity, and volatility—that determine data fitness. Addressing bias in data collection, cleaning, and device interaction is critical, alongside robust storage, tagging, and secure streaming. Continuous validation, risk management, and use of golden datasets strengthen reliability. Ultimately, lifecycle data quality management ensures AI delivers safe, compliant, and trustworthy results in healthcare.

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