Explainable AI for Deep Learning Models: Enhancing Trust and Transparency in Intelligent Medical Applications

As deep learning models become increasingly integral to various applications, opaque decision-making processes pose significant challenges in terms of transparency and trust. It is essential for organizations to fully understand AI decision-making processes with in-depth model monitoring and accountability, rather than placing blind trust in these systems. In the medical field, the impact of these decisions is significant, as physicians and patients can only fully trust AI systems if the origins of their results are reasonably communicated. This transparency allows for identifying errors and biases, fostering greater trust and reliability in AI-driven medical decisions. Explainable AI (XAI) provides rational explanations that help users comprehend how and why AI systems generate their outcomes, allowing human users to trust the results and output created by deep learning algorithms. This chapter aims to bridge the gap between the high performance of deep learning models and the need for interpretability. It explores the techniques used to achieve explainability in deep learning, illustrating their applications. The chapter highlights how XAI can significantly enhance diagnostic accuracy and reliability by enabling the identification of errors and biases in intelligent medical applications.