Building trustworthy AI for healthcare: a focus on explainability, uncertainty, and privacy - Zhang, Yanci (Nanyang Technological University, 2025)
Trustworthy AI is crucial in healthcare, as it is essential for ensuring stakeholder trust and the safe deployment of AI-driven decisions. Explainability is a key factor in trustworthy AI. Explainable AI (XAI) aims to make model predictions understandable to stakeholders. Understanding AI models' decision-making processes enhances stakeholder trust and ensures compliance with medical regulations. Many healthcare applications involve users uploading data via personal devices. To protect privacy, federated learning (FL) is employed, allowing models to be trained across distributed data sources without sharing raw data. However, this decentralized setting makes explainability challenging, especially in deriving a global explanation without direct data access. This limitation highlights a critical research gap in explainable federated learning.