Breast cancer is the most commonly diagnosed form of cancer in the world among women, with more than 2.3 million cases a year, and continues to be one of the main causes of cancer-related mortality. Precisely predicting whether this type of tumour will reappear remains one of the key challenges in oncology. To try and make progress in this field, an international team led by the Universitat Rovira i Virgili has developed an artificial intelligence model that brings together medical imaging data and clinical information to calculate the risk of tumour recurrence in a much more accurate and interpretative way.

In addition to linguistic prompts, large language models can also understand, interpret, and adapt their responses to heart frequency data. Dr. Morris Gellisch, previously of Ruhr University Bochum, Germany, and now at University of Zurich, Switzerland, and Boris Burr from Ruhr University Bochum verified this in an experiment. They developed a technical interface through which the physiological data can be transmitted to the language model in real time. The AI can also account for subtle physiological signals such as changes in heart activity. This opens new doors for use in medical and care applications. The work was published in the technical magazine Frontiers in Digital Health on September 26, 2025.

As recent Artificial Intelligence (AI) models’ capacity to understand and process long, complex sentences grows, the necessity for new semiconductor technologies that can simultaneously boost computation speed and memory efficiency is increasing. Amidst this, a joint research team featuring KAIST researchers and international collaborators has successfully developed a core AI semiconductor 'brain' technology based on a hybrid Transformer and Mamba structure, which was implemented for the first time in the world in a form capable of direct computation inside the memory, resulting in a four-fold increase in the inference speed of Large Language Models (LLMs) and a 2.2-fold reduction in power consumption.

New AI Controller Stabilizes Complex Economic Growth Models

Breakthrough research combines differential flatness theory and adaptive fuzzy control to manage economic uncertainties, offering a novel tool for policy simulation and stabilization.

[Greece/Tunisia/Italy] – A team of international researchers has developed a groundbreaking artificial intelligence (AI) method to control and stabilize the Uzawa-Lucas endogenous growth model, a cornerstone economic theory that describes the interaction between physical capital (like machinery) and human capital (like skills and knowledge). This novel approach, which functions without needing a precise mathematical model of the economy, could provide a powerful new tool for economists and policymakers to design more stable and effective economic policies.