Author: Dr. Robert Lookstein

Institution: Professor of Radiology and Surgery, Mount Sinai School of Medicine, New York, USA

Summary

This presentation explores the application of artificial intelligence (AI) in the diagnosis and management of acute pulmonary embolism (PE), particularly its potential to accelerate diagnosis, optimize patient management, and improve treatment decisions. By utilizing AI-enhanced CT scans and diagnostic systems, the Pulmonary Embolism Response Team (PERT) can identify and address high-risk PE cases more efficiently. The study shows that AI not only reduces diagnostic time but also increases accuracy and optimizes clinical decision-making.

AI in PE Diagnosis

•AI-Assisted CT Diagnosis: AI technology quickly identifies PE cases from CT scans, automatically calculating the right-to-left ventricular ratio (RV/LV ratio) and alerting the PERT team to potential high-risk PE patients. This automated diagnostic process significantly reduces the time required for diagnosis and improves efficiency.

•Integration with Electronic Medical Records (EMR): AI can seamlessly integrate with EMR systems, utilizing patient biomarkers, other imaging data, and vital signs to automatically generate relevant diagnostic suggestions. This real-time feedback increases accuracy and reduces false positives.

Optimizing Clinical Teams

• Real-Time Notifications and Communication: AI systems provide the PERT team with real-time patient information and imaging results through a HIPAA-compliant encrypted communication platform. Team members can receive this data on mobile devices, enabling them to make timely treatment decisions and promptly manage acute PE cases.

Clinical Research and Future Directions

• AI-Assisted Research: AI not only accelerates PE diagnosis but also helps identify potential clinical trial candidates, facilitating more research and trial enrollment. This promotes research efficiency and advances technological progress in PE treatment.

• Deep Learning and Treatment Choices: Combining AI with deep learning algorithms, it analyzes PERT registry data to provide clinicians with the best treatment options. By analyzing large datasets, AI determines the optimal treatment path for specific cases, further improving treatment success rates.

Conclusion

1. AI technology is gradually transforming the diagnosis and management of acute PE. Through automated imaging analysis and real-time feedback, PERT teams can handle acute PE cases more quickly and accurately.

2. AI not only reduces diagnostic time but also supports clinicians in making more optimized treatment decisions with data-driven insights.

3. As technology continues to evolve, AI will play an increasingly important role in the diagnosis, treatment selection, and clinical research for acute PE.

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