Robust privacy amidst innovation with large language models through a critical assessment of the risks.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 40112189

Abstract

OBJECTIVE: This study evaluates the integration of electronic health records (EHRs) and natural language processing (NLP) with large language models (LLMs) to enhance healthcare data management and patient care, focusing on using advanced language models to create secure, Health Insurance Portability and Accountability Act-compliant synthetic patient notes for global biomedical research.

Authors

  • Yao-Shun Chuang
    McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States.
  • Atiquer Rahman Sarkar
    Department of Computer Science, University of Manitoba, Winnipeg, Manitoba R3T 5V6, Canada.
  • Yu-Chun Hsu
    McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States.
  • Noman Mohammed
    Department of Computer Science, University of Manitoba, Winnipeg, Manitoba R3T 5V6, Canada.
  • Xiaoqian Jiang
    School of Biomedical Informatics, University of Texas Health, Science Center at Houston, Houston, TX, USA.

Keywords

External Resources

Popular Topics
Recent Journals