Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/57257, first published .
Assessing Racial and Ethnic Bias in Text Generation by Large Language Models for Health Care–Related Tasks: Cross-Sectional Study

Assessing Racial and Ethnic Bias in Text Generation by Large Language Models for Health Care–Related Tasks: Cross-Sectional Study

Assessing Racial and Ethnic Bias in Text Generation by Large Language Models for Health Care–Related Tasks: Cross-Sectional Study

Journals

  1. Stephan D, Bertsch A, Schumacher S, Puladi B, Burwinkel M, Al-Nawas B, Kämmerer P, Thiem D. Improving Patient Communication by Simplifying AI-Generated Dental Radiology Reports With ChatGPT: Comparative Study. Journal of Medical Internet Research 2025;27:e73337 View
  2. Kejriwal M. Designing Semantic Computing for Social Good Applications with Long Tails. International Journal of Semantic Computing 2025;19(02):247 View
  3. Burla L, Metzler J, Kalaitzopoulos D, Kamm S, Ormos M, Passweg D, Schraag S, Samartzis E, Samartzis N, Witzel I, Imesch P. Artificial intelligence in endometriosis care: A comparative analysis of large language model and human specialist responses to endometriosis-related queries. European Journal of Obstetrics & Gynecology and Reproductive Biology 2025;313:114625 View
  4. Esmaeilzadeh P. Baseline Evaluation of Claude Opus 4 for Diabetes Management: A Preliminary Assessment and Lessons for Implementation. Applied Clinical Informatics 2025;16(05):1881 View
  5. Ren R, Xu Y, Yao X, Cole S. Whose journey matters? Investigating identity biases in large language models (LLMs) for travel planning assistance. Current Issues in Tourism 2025:1 View