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Explainability Challenges in Medical AI: The Conceptual Hidden Cost of Machine Learning Preprocessing
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1
Autoren
2026
Jahr
Abstract
The novelty of this work lies in systematically connecting preprocessing practices with explainability challenges in healthcare artificial intelligence and suggesting approaches to balance performance with explainability. This highlights the need for careful design of preprocessing pipelines in medical artificial intelligence systems to ensure both reliable predictions and clinical trust.
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