Keywords
risk intelligence
Abstract
Artificial intelligence systems are increasingly deployed in risk-sensitive domains, including finance, computer vision, cybersecurity, and healthcare. Although these domains differ in data structure and decision objectives, they share common requirements for accurate prediction, interpretable reasoning, and evidence-based decision support. This article proposes a unified semantic-guided AI framework for cross-domain risk intelligence. The framework integrates semantic encoding, multi-source feature fusion, explainable machine learning, and evidence retrieval. In financial scenarios, it supports fraud and credit risk assessment; in visual scenarios, it enhances image recognition, remote sensing captioning, melanoma detection, and medical image interpretation; in cybersecurity, it assists anomaly detection and vulnerability prioritization; in clinical and biomedical scenarios, it supports epilepsy reasoning, neurodegenerative disease analysis, oncology risk assessment, transcriptomic regulation, medical device intelligence, and implant-related biomedical evaluation. The article argues that semantic guidance can improve the alignment between machine learning outputs and domain knowledge. By combining predictive models with explanation and evidence structures, the framework provides a general methodology for building trustworthy AI systems across high-stakes environments.
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