Generative Artificial Intelligence for Chest Radiograph Interpretation in the Emergency Department
JAMA Network Open2023Vol. 6(10), pp. e2336100–e2336100
Citations Over TimeTop 1% of 2023 papers
Jonathan Huang, Luke A Neill, Matthew T. Wittbrodt, David Melnick, Matthew Klug, Michael P. Thompson, John Bailitz, Timothy M. Loftus, Sanjeev Malik, Amit Phull, Victoria Weston, J. Alex Heller, Mozziyar Etemadi
Abstract
In a representative sample of emergency department chest radiographs, results suggest that the generative AI model produced reports of similar clinical accuracy and textual quality to radiologist reports while providing higher textual quality than teleradiologist reports. Implementation of the model in the clinical workflow could enable timely alerts to life-threatening pathology while aiding imaging interpretation and documentation.
Related Papers
- → Teleradiology: Two-Edged Sword or Friend of Radiology Practice?(2009)16 cited
- → Teleradiology: Technology and practice(1999)5 cited
- → The Evolution of a German Teleradiology System(1998)4 cited
- → Personal computer teleradiology: Comparing image quality of lateral cervical spine radiographs with conventional teleradiology(1993)22 cited
- Teleradiology: the practice of radiology enters Cyberspace.(1996)