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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.

Diagnostic services are central to clinical decision-making because imaging, laboratory testing, and cardiology diagnostics often determine the next step in diagnosis, treatment, or referral. Bottlenecks in these services can delay care pathways and increase wait times when demand rises faster than available capacity. Current forecasting approaches in diagnostic departments are often reactive and based on historical averages, recent appointment counts, or manual manager judgment. Such approaches may miss upstream signals such as referral surges, seasonal disease activity, and physician ordering behavior. This article proposes a predictive model for forecasting diagnostic service demand by integrating ambulatory referral volume, seasonal disease trends, physician ordering patterns, equipment availability, and historical appointment backlogs. The model is intended to support short- and medium-term capacity planning across diagnostic services. The proposed approach uses a supervised time-series forecasting framework, such as gradient boosting with temporal features or a recurrent neural architecture, trained on historical diagnostic order and scheduling data. Inputs would be engineered from referral streams, diagnostic ordering records, seasonal indicators, equipment schedules, and backlog measures. Conceptually, the model would generate daily or weekly demand forecasts for each diagnostic modality and service line. Forecasts would include uncertainty bounds and operational alerts when projected demand is expected to exceed available appointment capacity. The proposed predictive model could enable proactive capacity management in diagnostic departments. By anticipating demand before backlogs become severe, the model could support improved scheduling, better equipment utilization, and reduced patient waiting times.