Digital Twin approach to Clinical DSS with Explainable AI

Combining doctor expertise with AI improves consistency: The system addresses a key problem in healthcare—that clinical decisions vary significantly between doctors—by using machine learning to learn from collective medical expertise and create more standardized decision thresholds for diagnosis and treatment recommendations.

Explainability makes AI recommendations trustworthy for clinicians: By applying explainability techniques to the machine learning model, the system generates patient-specific explanations that show why a particular diagnosis or treatment is recommended, allowing doctors to understand and verify the AI's reasoning rather than treating it as a "black box."

Digital twins create personalized clinical insights: The approach generates a "digital twin" for each patient that synthesizes how different doctors would approach that specific patient's condition, providing customized decision support tailored to individual patient circumstances rather than one-size-fits-all recommendations.

Domain knowledge and data work together, not against each other: Rather than replacing doctor judgment with pure data, the system integrates established medical knowledge (decision thresholds from clinical practice) with data-driven learning, creating a hybrid approach that respects both clinical expertise and empirical evidence.

Practical application demonstrated in liver disease diagnosis: The authors validate their approach using liver disease risk assessment as a concrete example, suggesting the framework could be implemented in real clinical settings to improve diagnostic consistency and decision quality.