EurekAlert! In the Spotlight - Artificial Intelligence

Welcome to In the Spotlight, where each month we shine a light on something exciting, timely, or simply fascinating from the world of science.

This month, we’re focusing on artificial intelligence (AI), a topic that continues to capture attention everywhere. Here, you’ll find the latest research news, insights, and discoveries shaping how AI is being developed and used across the world.

Latest News Releases

Updates every hour. Last Updated: 3-Nov-2026 14:17 ET (3-Nov-2026 19:17 GMT/UTC)

Feeding data to AI to speed up drug discovery

University of Michigan
Peer-Reviewed Publication

University of Michigan researchers have created an open-access database of more than 50,000 chemical reactions - the largest such database to date - and a much needed source of organic chemistry reactions needed for now-underfed artificial intelligence. In related discoveries while testing the reactions the team found solutions that address supply chain and natural resource shortages.

Journal: Journal of the American Chemical Society

Baycrest study reveals the brain remembers sights and sounds differently

Baycrest Corporate Centre for Geriatric Care

New Baycrest research reveals that the brain remembers what we see and what we hear in different ways. Visual memories tend to retain fine details, while auditory memories are more likely to preserve the overall meaning of an experience. The findings shed new light on how the brain reconstructs memories and could help inform future approaches to learning, aging and rehabilitation.

Journal: JNeurosci

Artificial intelligence in genitourinary pathology: A translational readiness map

Xia & He Publishing Inc.

Artificial intelligence (AI) translation in genitourinary (GU) pathology has progressed unevenly across organs and tasks. This review addresses a central clinical question: which GU pathology AI applications are deployment-ready, which require further validation, and what frameworks can guide safe implementation? We synthesize evidence across GU organs and introduce pragmatic translation frameworks to guide deployment and prioritize translational research.

Journal: Journal of Clinical and Translational Pathology

SwRI, St. Mary’s University to improve metabolic cost prediction using ENABLE technology

Southwest Research Institute
Business Announcement

SAN ANTONIO — June 23, 2026 — Southwest Research Institute (SwRI) and St. Mary’s University are collaborating to expand and validate a system to estimate metabolic cost prediction for evaluating movement efficiency to improve performance or rehabilitation. The team will use ENABLE™, an SwRI-developed markerless motion capture technology, musculoskeletal modeling, and machine learning to improve metabolic cost prediction for healthcare. The project is supported by a $127,750 grant from the St. Mary’s-SwRI Technology & Applied Research (S²TAR) program, which fosters collaborations between researchers from both organizations.

Changes in AI mammogram risk scores over time help predict future breast cancer

Radiological Society of North America
Peer-Reviewed Publication

Using artificial intelligence (AI), researchers found that image-based risk scores for breast cancer derived from screening mammograms evolve over time and differ between women who develop cancer and those who do not, opening the door to a new era of dynamic breast cancer risk assessment.

Journal: Radiology

New infrastructure model prioritizes disaster spending for vulnerable cities

University of Houston
Peer-Reviewed Publication

A new mathematical model developed at the University of Houston can help cities identify their most critical vulnerabilities before disaster strikes.

Journal: Computers & Industrial Engineering

Forgetting may be the secret to better AI language learning

Max Planck Institute for Psycholinguistics
Peer-Reviewed Publication

Giving AI a human-like memory limitation may actually help it learn language better. In their new proof-of-principle study, Abishek Thamma (University of Amsterdam) and Micha Heilbron (Max Planck Institute for Psycholinguistics) show that small language models equipped with a transient memory learn grammar more efficiently when trained on child-scale amounts of language input. The findings demonstrate how insights from psycholinguistics can inspire new approaches to AI learning.

Journal: Computational Linguistics