Keywords
clinical reasoning
Abstract
Trustworthy AI across clinical, visual, urban, and financial domains requires evidence alignment, transparent reasoning, and domain-aware representation learning. This topic synthesizes evidence-intensive epilepsy reasoning, forensic vision-language compression, instance-aware super-resolution, financial fraud risk assessment, medical image interpretation, oncology evidence, neurodegenerative disease AI, medical device systems, and urban risk governance. The central challenge is to connect heterogeneous evidence without reducing domain-specific knowledge to generic prediction outputs. Clinical reasoning requires traceable medical evidence; visual analytics requires robust image restoration and token-efficient multimodal interpretation; urban governance requires cross-scale spatial and public health evidence; financial risk assessment requires explainable anomaly detection. By organizing these directions into a shared literature framework, this topic supports the development of cross-domain AI systems that remain interpretable, evidence-grounded, and operationally useful.
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