Explainable AI Framework for Building Trust in AI—Driven Patient Engagement Marketing for Chronic Disease Management

Abstract

The concept of AI-driven patient engagement has reinvented the future of chronic disease management by providing customized information and support. Nonetheless, intimidating complex artificial intelligence models are hard to adopt since they are opaque or black-box. This transparency is especially an issue concerning the health industry where credibility and responsibility are key attributes. The proposed study uses a novel framework grounded on the newly emerging approach known as Explainable AI (XAI) to build trust in AI-enabled solutions to enhance patient engagement in the management of chronic diseases. The model combines approaches like SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) to explain why the AI gives a specific recommendation. The system means that patients and healthcare professionals can recognize, comprehend, and respond to AI-mediated interventions better by allowing them to explain their actions clearly and concisely. The article outlines the conceptual framework of the framework, discusses the way the framework can be applied to chronic conditions like diabetes and hypertension, and critically reviews how the framework addresses questions of ethical and legal implications, such as bias, privacy, and accountability. On the whole, this study can be viewed as a major step toward a transparent, trusting, and ethically grounded AI ecosystem that aids in chronic disease management.

Ähnliche Arbeiten