A LIME-based Explainable AI for Healthcare IoT: Building Trust in Clinical Decision-Making
DOI:
https://doi.org/10.48001/978-81-980647-5-2-3Keywords:
Explainable AI (XAI), Healthcare IoT, Explainable Clinical Decision, LIMEAbstract
The integration of Artificial Intelligence (AI) and Internet of Things (IoT) devices in healthcare offers vast potential for personalized medicine, remote monitoring, and early disease detection. However, complex Machine Learning (ML) models embedded in these systems often operate as "black boxes," hindering trust and transparency in critical medical decisions. Explainable AI (XAI) emerges as a key solution, aiming to demystify ML models and build trust in healthcare IoT applications. This paper explores the current challenges and opportunities in implementing XAI for healthcare IoT, proposing an architecture and methodologies for explainable clinical decision-making. We discuss promising XAI techniques, the integration of user interfaces for interactive explanations, and potential future directions for this crucial field.
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