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The Role of Data Quality for Reliable AI Performance in Medical Applications
7
Zitationen
2
Autoren
2024
Jahr
Abstract
Data are an indispensable asset for industries and organizations, serving as the foundation for informed decision-making and strategic planning. In today’s data-driven world, where machine learning (ML)/artificial intelligence (AI) models are continually evolving and being adopted across numerous real-world applications, the effective utilization of data is paramount. High-quality data are critical for training ML/AI algorithms, as they directly influence the reliability and accuracy of their results <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref>. This is particularly crucial in the healthcare industry, where data quality is essential for ensuring precise diagnostics, effective treatment plans, and improved patient outcomes <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref>. Consequently, maintaining data integrity throughout the ML/AI development and deployment process is of utmost importance to achieve these objectives.
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