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Radiology reporting generates critical findings that require urgent communication across increasingly complex clinical workflows. Static worklist triage does not fully exploit report content, patient risk, order context, or prior escalation behaviour. Manual prioritisation and rule-based STAT flags may overlook subtle or unexpected abnormalities embedded in free-text reports. These approaches also fail to adapt to real-time patient vulnerability and institutional communication patterns. This article proposes an AI-based radiology operations framework that continuously extracts clinically actionable findings from narrative reports. The framework fuses those findings with order urgency, patient risk profiles, and historical escalation patterns to support dynamic worklist prioritisation. The system comprises an NLP report analysis module, an urgency-risk fusion engine, a historical escalation learner, a prioritisation decision engine, and a real-time notification dashboard. Each component is designed to support explainable, auditable, and workflow-sensitive prioritisation. The proposed system could reduce delays in acknowledging critical imaging results while balancing radiologist workload. Its value would depend on careful governance, transparent priority justifications, and human-in-the-loop feedback. An adaptive AI-based worklist system offers a pathway toward safer, data-driven radiology operations. By integrating narrative findings, clinical context, and historical escalation behaviour, such a framework could strengthen critical result management.