The Role of Data Quality for Reliable AI Performance in Medical Applications

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 [1]. This is particularly crucial in the healthcare industry, where data quality is essential for ensuring precise diagnostics, effective treatment plans, and improved patient outcomes [2]. Consequently, maintaining data integrity throughout the ML/AI development and deployment process is of utmost importance to achieve these objectives.