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Artificial neural networks are frequently applied in health care, but their inherent opacity necessitates the development of explainable AI and neuro-symbolic approaches to ensure safety and transparency in clinical decision-making as described in [1], [2], and [3].
Facts (3)
Sources
Neurosymbolic AI: The Future of Artificial Intelligence - LinkedIn linkedin.com 1 fact
claimNeural networks often function as black boxes, making it difficult to interpret their decisions, which creates a need for explainability in critical applications like healthcare and finance.
What Changes Can Neuro-Symbolic AI Bring to the World - IJSAT ijsat.org 1 fact
claimNeuro-Symbolic AI integrates neural networks with symbolic reasoning to improve transparency, decision-making, and safety in applications such as healthcare and autonomous vehicles.
Unlocking the Potential of Generative AI through Neuro-Symbolic ... arxiv.org 1 fact
claimThe opacity of neural networks creates challenges for critical applications requiring explanation, such as healthcare, finance, legal frameworks, and engineering.