This review systematically examines the integration of machine learning (ML) and artificial intelligence (AI) in nanomedicine for cancer drug delivery. It demonstrates how ML algorithms—including support vector machines, neural networks, and deep learning models—are revolutionizing nanoparticle design, drug release prediction, and personalized therapy planning. The article outlines the complete ML workflow from data acquisition to model interpretation, compares key algorithms, and presents real-world case studies spanning multidrug carrier optimization and cancer diagnostics. While highlighting substantial preclinical advances, the authors identify critical barriers to clinical translation such as data heterogeneity, model opacity, and regulatory challenges. The review concludes with a forward-looking roadmap emphasizing data standardization, explainable AI, and clinical validation to bridge the gap between computational innovation and patient-ready nanomedicine.

In media coverage, individuals’ membership in minority groups, such as their country of origin, is often explicitly mentioned. In contrast, majority membership is usually not highlighted. Does this reflect prejudice against minorities? A study with over 900 participants suggests that it does not. Instead, a fundamental cognitive principle of differentiation drives this pattern: People tend to notice and communicate rare or striking characteristics more readily than common and redundant ones. Artificial intelligence shows the same tendency and even amplifies it. This is reported by a team from the Social Cognition Lab at Ruhr University Bochum, Germany, led by Dr. Anna Schulte in the journal Social Psychological and Personality Science on December 1, 2025.

A monitoring system devised by the University of Cordoba ascertains the flowering stages of each hive, with high precision, exploiting data on bees' behavior

Huaqiao university and ELSPublishing hosts 2025 IEEE the 6th International Conference on Computer, Big Data and Artificial Intelligence (IEEE ICCBD+AI 2025) in Xiamen.

Chinese researchers have taken a fresh look at one of the biggest challenges in precision manufacturing: understanding and controlling the many different errors that affect the accuracy of machine tools. Their review, published in the International Journal of Extreme Manufacturing, explains why these errors are becoming increasingly complex to manage and how new technologies can help.

Nashville, TN & Williamsburg, VA – 24 Nov 2025 – A new study published in Artif. Intell. Auton. Syst. delivers the first systematic cross-model analysis of prompt engineering for structured data generation, offering actionable guidance for developers, data scientists, and organizations leveraging large language models (LLMs) in healthcare, e-commerce, and beyond. Led by Ashraf Elnashar from Vanderbilt University, alongside co-authors Jules White (Vanderbilt University) and Douglas C. Schmidt (William & Mary), the research benchmarks six prompt styles across three leading LLMs to solve a critical challenge: balancing accuracy, speed, and cost in structured data workflows.

Structured data—from medical records and receipts to business analytics—powers essential AI-driven tasks, but its quality and efficiency depend heavily on how prompts are designed. “Prior research only scratched the surface, testing a limited set of prompts on single models,” said Elnashar, the study’s corresponding author and a researcher in Vanderbilt’s Department of Computer Science. “Our work expands the horizon by evaluating six widely used prompt formats across ChatGPT-4o, Claude, and Gemini, revealing clear trade-offs that let practitioners tailor their approach to real-world needs.”