Artificial intelligence (AI) in healthcare is only as trustworthy as the data it learns from and the context in which that data is used. This white paper explains why global data quality is the foundation for decision support in Healthcare AI applications and outlines the practical steps that health systems, payers, and app developers can take now. Quality data in healthcare enables safer decisions for payers, clinicians, and providers, reduces bias, fosters trust and accountability between clinicians and patients, supports reliable care across networks that share the data, and aligns with industry and global standards. This approach aligns with the IEEE Standards Association (IEEE SA), the IEEE Industry Connections program (IEEE IC), the Health Data, Technology, and Interoperability Final Rule (HTI-1), the Trusted Exchange Framework and Common Agreement (TEFCA), and WHO guidance on AI ethics to create a clear “why now?” As health data moves across systems and borders, data quality and traceability determine whether AI protects patients or puts them at risk, making responsible, measurable cross-border use essential.
- Status
- Published White Paper
- History
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- Published:
- 2026-07-28
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