Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/50935, first published .
Evaluation of a Natural Language Processing Approach to Identify Diagnostic Errors and Analysis of Safety Learning System Case Review Data: Retrospective Cohort Study

Evaluation of a Natural Language Processing Approach to Identify Diagnostic Errors and Analysis of Safety Learning System Case Review Data: Retrospective Cohort Study

Evaluation of a Natural Language Processing Approach to Identify Diagnostic Errors and Analysis of Safety Learning System Case Review Data: Retrospective Cohort Study

Journals

  1. Zheng Y, Yan Y, Chen S, Cai Y, Ren K, Liu Y, Zhuang J, Zhao M. Integrating retrieval-augmented generation for enhanced personalized physician recommendations in web-based medical services: model development study. Frontiers in Public Health 2025;13 View
  2. Mayampurath A, Rosado A, Romo E, Silberman P, Patel J, Jankowski S, Maas M, Holl J, Liberman A, Prabhakaran S. Identification of neurological text markers associated with risk of stroke. Journal of Stroke and Cerebrovascular Diseases 2025;34(8):108376 View
  3. Howard A, Reza N, Green P, Yin M, Duffy E, Mwandumba H, Gerada A, Hope W. Artificial intelligence and infectious diseases: tackling antimicrobial resistance, from personalised care to antibiotic discovery. The Lancet Infectious Diseases 2025 View
  4. Khan S, Bradford A, Cifra C, Singh H. Two decades of diagnostic safety research: advances, challenges, and next steps. Diagnosis 2025;12(4):549 View
  5. Mwogosi A, Simba R. Meta-Analysis of Electronic Health Records and Their Effect on Diagnostic and Medication Errors in Healthcare. Intelligence-Based Medicine 2025:100325 View