Revolutionizing Medical Diagnostics with Transparent AI-Driven Decision Support Systems

This study introduces an innovative AI-Driven Decision Support System (DSS) for revolutionizing healthcare diagnostics, emphasizing the use of Explainable AI (XAI) to enhance transparency and trust in medical decision-making. The proposed system integrates machine learning and deep learning algorithms to analyze and interpret complex medical data, providing clinicians with clear, understandable insights that support diagnostic accuracy and patient outcomes. The system was evaluated on a dataset of 25,000 patient records across multiple diagnostic domains, including cardiology, oncology, and radiology. Key performance metrics showed a significant improvement in diagnostic precision and explain ability. The model achieved an accuracy of 94.8% in diagnosing cardiac conditions, with an explanation accuracy of 92.1%, measured by how well the XAI techniques correlated with expert evaluations. For oncology cases, the system demonstrated an 87.5<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> reduction in false positives, while maintaining an overall accuracy of 93.2%. In terms of clinician trust, surveys indicated a 35% increase in confidence when using the XAI-based system compared to traditional black-box models. Additionally, the time to generate diagnostic explanations was reduced by 40%, improving clinical workflow and decision-making efficiency. These results underscore the transformative potential of integrating XAI into healthcare decision support systems, providing not only accurate predictions but also interpretable insights that enhance trust and usability in medical diagnostics.