The field of AI research is witnessing significant developments in the analysis of labor markets and biomedical data. Recent studies have highlighted the importance of addressing gender stereotypes and cultural inaccuracies in AI-generated images, particularly in professional roles. Moreover, the creation of large-scale datasets such as ArabJobs and MEDAKA has enabled researchers to investigate linguistic, regional, and socio-economic variations in labor markets and biomedical information. The application of large language models (LLMs) has also shown promise in extracting pharmacokinetic data from complex tables and documents, as well as in predicting veterinary safety outcomes. Noteworthy papers in this area include: ArabJobs, a multinational corpus of Arabic job ads, which offers opportunities for future research on gender representation and occupational structure. AutoPK, a novel framework for extracting pharmacokinetic data from complex tables, which has demonstrated significant improvements in precision and recall over direct LLM baselines. ALARB, an Arabic legal argument reasoning benchmark, which has been used to evaluate the reasoning capabilities of LLMs in the Arabic legal domain. Extracting O*NET features from the NLx corpus has also enabled the construction of public-use aggregate labor market data, providing valuable insights for research and education. Predictive modeling and explainable AI for veterinary safety profiles have shown accurate and interpretable predictions of veterinary safety outcomes, supporting early detection of high-risk drug-event profiles and informing regulatory decision-making.