"Explainable AI (XAI) in Healthcare: Enhancing Predictive Accuracy and Risk Assessment for Patient Outcomes" (KEY IJP************753)

• Dr. Suneel Pappala,A.prakashini ,Dr. K.v.naganjanayulu,Mayank Sharma

Abstract

Explainable AI (XAI) enhances transparency, interpretability, and accountability in AI-driven decision-making. By making complex models, such as deep learning networks, more understandable, XAI fosters trust, ensures fairness, and aids regulatory compliance. Key techniques include SHAP (Shapley Additive Explanations), LIME (Local Interpretable Model-Agnostic Explanations), and attention mechanisms. In healthcare, XAI improves diagnostics, treatment recommendations, and patient risk assessments by providing interpretable insights. Challenges include balancing accuracy with interpretability, mitigating bias, and ensuring human comprehension. Future advancements will integrate hybrid models, interactive explanations, and regulatory frameworks, ensuring AI remains a reliable tool for critical applications like medicine and finance.