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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Advancing ethical AI in healthcare through interpretability
Yilin Ning1,2, Mingxuan Liu1,2, Nan Liu1,2,3,4,5
1Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.
Abstract:
Interpretability is essential for building trust in health artificial intelligence (AI), but ensuring trustworthiness requires addressing broader ethical concerns, such as fairness, privacy, and reliability. This opinion article discusses the multilayered role of interpretability and transparency in addressing these concerns by highlighting their fundamental contribution to the responsible adoption and regulation of health AI.
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