Understanding Explainability in Medical Imaging

Abstract

The integration of artificial intelligence (AI) into medical imaging has revolutionized healthcare offering unparalleled advancements in diagnosis, treatment planning, and patient care. However, alongside these advancements arises the critical need for transparency and accountability in AI-driven systems. This introduction explores the concept of explainability within the context of medical imaging emphasizing its vital role in fostering trust, acceptance, and widespread adoption of AI technologies in healthcare. Explainability in medical imaging refers to the ability of AI models or systems to provide understandable justifications for their decisions or predictions. It entails elucidating the underlying rationale behind AI outputs enabling clinicians and stakeholders to comprehend, trust, and validate the insights offered by these AI-enabled tools. By bridging the gap between the complex methodologies of AI and the interpretability requirements of clinical decision making, explainability serves as a crucial conduit for enhancing transparency and facilitating informed decision making in healthcare settings.

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