Assessing Laterality Errors in Radiology: Comparing Generative Artificial Intelligence and Natural Language Processing.

Journal: Journal of the American College of Radiology : JACR
PMID: 38960083

Abstract

PURPOSE: We compared the performance of generative artificial intelligence (AI) (Augmented Transformer Assisted Radiology Intelligence [ATARI, Microsoft Nuance, Microsoft Corporation, Redmond, Washington]) and natural language processing (NLP) tools for identifying laterality errors in radiology reports and images.

Authors

  • Anjaneya Singh Kathait
    Research Fellow, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts. Electronic address: akathait@mgh.harvard.edu.
  • Emiliano Garza-Frias
    Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
  • Tejash Sikka
    Research Fellow, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
  • Thomas J Schultz
    Research Fellow, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Senior Director, Enterprise Medical Imaging, Mass General Brigham AI, Boston, Massachusetts.
  • Bernardo Bizzo
  • Mannudeep K Kalra
  • Keith J Dreyer
    Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Mass General Brigham Data Science Office (DSO), Boston, MA, United States.

Keywords

External Resources

Popular Topics
Recent Journals