Search

The Berlin definition of ARDS provides standardized diagnostic criteria based on acute onset within one week of a known insult, bilateral chest imaging opacities not explained by other causes, respiratory failure not due to cardiac issues or fluid overload, and impaired oxygenation measured by the PaO₂/FiO₂ ratio, enabling consistent identification in intensive care; however, its clinical use is limited by variability in imaging interpretation and the need for rapid decision-making, often causing delays and inconsistent diagnoses. Current practice relies heavily on subjective assessment of chest X-rays and limited integration of clinical notes and laboratory trends, resulting in moderate inter-observer agreement and reduced diagnostic reliability. To overcome these challenges, a multimodal transformer framework is proposed that integrates chest X-rays, clinical notes, and laboratory data using vision transformers, BERT-based text encoders, and temporally aware lab embeddings, with cross-modal attention enabling interaction across data types and a fusion module producing final ARDS probability estimates. This integrated approach improves diagnostic accuracy by combining complementary information, enhances interpretability through attention mechanisms, and offers a more objective and timely method for ARDS detection, with potential to support earlier intervention and better outcomes in critically ill patients.

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.