A Novel Framework for Explainable AI in Healthcare Diagnostics
Abstract
The adoption of Artificial Intelligence (AI) in healthcare has significantly enhanced diagnostic accuracy and efficiency. However, the lack of transparency in AI models hinders trust and widespread acceptance. This paper proposes a novel framework for Explainable AI (XAI) tailored for healthcare diagnostics. The framework combines deep learning techniques with interpretable models to provide clinicians with actionable insights. Case studies in radiology and pathology are used to demonstrate the framework's efficacy, showing improved diagnostic accuracy while maintaining interpretability. The proposed solution aims to bridge the gap between AI performance and clinical trust, fostering a symbiotic relationship between technology and healthcare professionals.
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