Algorithm Transparency and Interpretability for AI-Based Medical Imaging

Abstract

Transparency and interpretability of algorithms are essential factors required while developing and implementing AI-based medical imaging systems. Algorithm transparency ability lies in understanding and interpreting how an AI algorithm arrives at its decisions or predictions. This is the main challenge in implementing AI-based medical imaging techniques. Methods like explainable AI (XAI) are utilized to show how the algorithm makes decisions and points out important parts of the input data. These methods include feature visualization, attention mechanisms, artificial neural networks, and conceptNet. In our work, we perform extensive algorithmic testing and evaluation on various datasets, including external testing using real-world clinical data. The algorithm's advantages and disadvantages require openness in reporting evaluation results

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