Artificial intelligence in global health equity: an evaluation and discussion on the application of ChatGPT, in the Chinese National Medical Licensing Examination.

Background: The demand for healthcare is increasing globally, with notable disparities in access to resources, especially in Asia, Africa, and Latin America. The rapid development of Artificial Intelligence (AI) technologies, such as OpenAI's ChatGPT, has shown promise in revolutionizing healthcare. However, potential challenges, including the need for specialized medical training, privacy concerns, and language bias, require attention. Methods: To assess the applicability and limitations of ChatGPT in Chinese and English settings, we designed an experiment evaluating its performance in the 2022 National Medical Licensing Examination (NMLE) in China. For a standardized evaluation, we used the comprehensive written part of the NMLE, translated into English by a bilingual expert. All questions were input into ChatGPT, which provided answers and reasons for choosing them. Responses were evaluated for "information quality" using the Likert scale. Results: ChatGPT demonstrated a correct response rate of 81.25% for Chinese and 86.25% for English questions. Logistic regression analysis showed that neither the difficulty nor the subject matter of the questions was a significant factor in AI errors. The Brier Scores, indicating predictive accuracy, were 0.19 for Chinese and 0.14 for English, indicating good predictive performance. The average quality score for English responses was excellent (4.43 point), slightly higher than for Chinese (4.34 point). Conclusion: While AI language models like ChatGPT show promise for global healthcare, language bias is a key challenge. Ensuring that such technologies are robustly trained and sensitive to multiple languages and cultures is vital. Further research into AI's role in healthcare, particularly in areas with limited resources, is warranted.

Molecular characterization and expression analysis of selenoprotein W gene in rainbow trout (Oncorhynchus mykiss) with dietary selenium levels.

Selenium (Se) plays an essential role in the growth of fish and performs its physiological functions mainly through incorporation into selenoproteins. Our previous studies suggested that the selenoprotein W gene (selenow) is sensitive to changes in dietary Se in rainbow trout. However, the molecular characterization and tissue expression pattern of selenow are still unknown. Here, we revealed the molecular characterization, the tissue expression pattern of rainbow trout selenow and analyzed its response to dietary Se. The open reading frame (ORF) of the selenow gene was composed of 393 base pairs (bp) and encodes a 130-amino-acid protein. The 3' untranslated region (UTR) was 372 bp with a selenocysteine insertion sequence (SECIS) element. Remarkably, the rainbow trout selenow gene sequence was longer than those reported for mammals and most other fish. A ß1-α1-ß2-ß3-ß4-α2 pattern made up the secondary structure of SELENOW. Furthermore, multiple sequence alignment revealed that rainbow trout SELENOW showed a high level of identity with SELENOW from Salmo salar. In addition, the selenow gene was ubiquitously distributed in 13 tissues with various abundances and was predominantly expressed in muscle and brain. Interestingly, dietary Se significantly increased selenow mRNA expression in muscle. Our results highlight the vital role of selenow in rainbow trout muscle response to dietary Se levels and provide a theoretical basis for studies of selenow.