Thomas E. Hutson, D.O., Pharm.D., Ph.D., chief of the Hematology Oncology Division in the Department of Internal Medicineat Texas Tech University Health Sciences Center (TTUHSC) and director of the University Medical Center (UMC) Cancer Center, shared groundbreaking findings from the landmark CLEAR (Clinical trial Comparing Lenvatinib with Ecerolimus or Pembrolizumab in Renal Cell Carcinoma) study. The findings underscore the critical role of tumor size reduction in improving survival outcomes for patients with advanced renal cell carcinoma (aRCC).

Divorce can take a toll on children’s mental health, but new research from The University of Texas at Arlington reports that its effects may last far longer than expected, potentially increasing the risk of serious health issues decades later. According to findings by social work Associate Professor Philip Baiden recently published in the journal PLoS One, Americans aged 65 and older who experienced their parents divorcing as children were more likely to suffer a stroke compared to their peers—one in nine as compared to one in 15 whose parents did not divorce. 

Researchers from the University of Navarra's Data Science and Artificial Intelligence Institute (DATAI) have developed a new AI framework to reduce bias in critical decision-making areas such as health, education, and recruitment. Their methodology optimizes machine learning models to ensure fairness by addressing inequalities related to race, gender, and socioeconomic status, among other possible algorithmic discriminations. Published in Machine Learning, the study combines conformal prediction techniques with evolutionary learning to achieve reliable and unbiased AI predictions. The researchers tested their approach on real-world datasets, demonstrating that it reduces discrimination without compromising accuracy. Their work provides policymakers and businesses with AI models that balance efficiency and fairness, aligning with ethical AI principles and legal requirements. The team has publicly made their code and data available to promote transparency and further research in responsible AI development.