Realizing Impact of Artificial Intelligence in Real World Enhances Public Health

Affiliations

¹ Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, United States.
² Retired, Formerly from the National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, United States.

Editorial

Huixiao Hong et al. Exp Biol Med (Maywood). 2025.

. 2025 Jun 27:250:10700.

doi: 10.3389/ebm.2025.10700. eCollection 2025.

¹ Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, United States.
² Retired, Formerly from the National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, United States.

No abstract available

Keywords: AI; bioinformatics; data; deep learning; machine learning.

Conflict of interest statement

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Editorial on the Research Topic Realizing Impact of Artificial Intelligence in Real World Enhances Public Health

1. Dong F, Hardy B, Liu J, Mohoric T, Guo W, Exner T, et al. Development of a comprehensive open access “molecules with androgenic activity resource (MAAR)” to facilitate risk assessment of chemicals. Exp Biol Med (2024) 249:10279. 10.3389/ebm.2024.10279 - DOI - PMC - PubMed
1. Li D, Wu L, Lin Y-C, Huang H-Y, Cotton E, Liu Q, et al. Enhancing pharmacogenomic data accessibility and drug safety with large language models: a case study with Llama3.1. Exp Biol Med (2024) 249:10393. 10.3389/ebm.2024.10393 - DOI - PMC - PubMed
1. Rogers P, McCall T, Zhang Y, Reese J, Wang D, Tong W, et al. AI-powered topic modeling: comparing LDA and BERTopic in analyzing opioid-related cardiovascular risks in women. Exp Biol Med (2025) 250:10341. 10.3389/ebm.2024.10341 - DOI - PMC - PubMed
1. Ma L, Chen R, Ge W, Rogers P, Lyn-Cook B, Hong H, et al. AI-powered topic modeling: comparing LDA and BERTopic in analyzing opioid-related cardiovascular risks in women. Exp Biol Med (2025) 250:10389. 10.3389/ebm.2025.10389 - DOI - PMC - PubMed
1. Liu J, Li J, Li Z, Dong F, Guo W, Ge W, et al. Developing predictive models for μ opioid receptor binding using machine learning and deep learning techniques. Exp Biol Med (2025) 250:10359. 10.3389/ebm.2025.10359 - DOI - PMC - PubMed