SIR 2024
General IR
Daniel Kushner, MD
Radiology Resident
Temple University Hospital
Financial relationships: Full list of relationships is listed on the CME information page.
Richard Graf, MD (he/him/his)
Radiology Resident
Temple University Hospital
Financial relationships: Full list of relationships is listed on the CME information page.
Gary Cohen, MD
Chair, Department of Radiology
Temple University Hospital
Disclosure information not submitted.
Joseph Panaro, MD
Clinical Associate Professor, Interventional Radiology
Temple University Hospital
Disclosure information not submitted.
Shane Lee, MD
Interventional Radiology Attending
Temple University Health System
Disclosure information not submitted.
Derek Lee, MD
Clinical Assistant Professor, Vascular and Interventional Radiology
Temple University Hospital
Disclosure information not submitted.
Perry Gerard, MD, FACR, MBA
Professor of Radiology
New York Medical College- Westchester Medical Center
Disclosure information not submitted.
Jared Meshekow, MD MPH
Clinical Assistant Professor, Vascular and Interventional Radiology
Temple University Hospital
Disclosure information not submitted.
Pulmonary embolism (PE) is a common and potentially life threating condition requiring prompt diagnosis and intervention. Tertiary academic medical centers often employ specialized Pulmonary Embolization Response Teams (PERTs) to provide timely and coordinated care in order to minimize time to intervention. Integrating artificial intelligence (AI), such as the AiDoc solution, into PERT workflows shows promise for interdisciplinary collaborative decision-making and improving patient outcomes by allowing for more rapid patient triage, prompt diagnosis, and the initiation of potentially life-saving interventions.
Clinical Findings/Procedure Details:
This study presents insights into the introduction and utilization of the AiDoc artificial intelligence within a PERT at a tertiary academic medical center. AiDoc utilizes deep learning algorithms to aid in the rapid interpretation of medical imaging, enabling prompt awareness of acute findings, efficient identification of high-risk patients, and optimization of treatment strategies.
Key findings include:
The inclusion and implementation of AiDoc within a PERT at a tertiary academic medical center have yielded substantial benefits. These include expedited and accurate PE diagnosis, enhanced risk assessment, improved team communication through a centralized platform, and continued tracking of patient progress. The integration of AiDoc exemplifies the potential of AI to facilitate and optimize patient care, particularly in emergent clinical settings including PE management.
Teaching Points: