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: 25-Jun-2026 01:16 ET (25-Jun-2026 05:16 GMT/UTC)

23-Jun-2026

Looking at AI startups to predict which jobs AI will affect

PNAS Nexus
Peer-Reviewed Publication

A study of funded AI startups provides a glimpse of which jobs may be most affected by AI.

Journal: PNAS Nexus

23-Jun-2026

Graph learning and single-cell genomics may unlock more accurate gene regulatory network inference

Science Exploration Press
Peer-Reviewed Publication

A new graph-based deep learning framework may improve the reconstruction of gene regulatory networks from single-cell RNA sequencing (scRNA-seq) data by integrating global network structure learning with biologically informed statistical modeling, according to a study published in Computational Biomedicine.

Journal: Computational Biomedicine
Funder: National Natural Science Foundation of China, Talent Program of Xinjiang Autonomous Region–Youth Outstanding Talent and Youth Innovative Talent, Xinjiang Autonomous Region Tianshan Innovation Team Program

23-Jun-2026

AI enables breakthrough in MRI efficiency

Technion-Israel Institute of Technology
Peer-Reviewed Publication

Researchers from the Technion and leading U.S. medical institutions have developed ELITE, a new AI-powered MRI method that dramatically improves the speed and quality of dynamic imaging. By combining artificial intelligence with advanced mathematical modeling, the technique generates MRI images at a rate of one image per second, enabling near-continuous tracking of contrast-agent flow through tissue. In a study involving 54 breast cancer patients, ELITE improved tumor visibility while maintaining high image quality and diagnostic sensitivity. The technology could help physicians detect and characterize tumors more accurately, increase MRI scanning capacity, and potentially be applied to imaging of the brain, head, and neck, as well as other medical imaging platforms.

Journal: Nature Communications

23-Jun-2026

Rayleigh-driven ethanol cluster inference based on non-contact optical sensing and deep learning

Editorial Office of Opto-Electronic Journals Group
Peer-Reviewed Publication

Detecting gas molecules through light scattering is fundamentally limited by weak signals and environmental noise. To address this, researchers developed a non-contact optical sensing strategy based on inverse inference that avoids direct measurement of scattered light. Instead, it analyzes subtle distortions in a transmitted laser beam after interaction with gas molecules and decodes them to infer molecular information. By combining a graphene-based diffractive lens with deep learning, the system enables stable and precise ethanol sensing.

Journal: Opto-Electronic Advances
Funder: National Research Foundation of Korea, Korea Environment Industry & Technology Institute, Korea Ministry of Environment

23-Jun-2026

ERC Advanced Grant for TU Graz researcher Thomas Pock

Graz University of Technology
Grant and Award Announcement

Thomas Pock, head of the Institute of Visual Computing at Graz University of Technology (TU Graz), has been awarded an Advanced Grant from the European Research Council (ERC) for his project EAGLE – Efficient Algorithms for Generative Learning. In his research, the computer scientist aims to develop novel generative learning methods and algorithms. He will receive funding of 2.5 million euros for this, with the project running for five years.

Funder: European Research Council

23-Jun-2026

New framework renders AI trustworthy for cancer subtyping

Vanderbilt University Medical Center
Peer-Reviewed Publication

Reported June 23 in Nature Biomedical Engineering, researchers at Vanderbilt Health and centers in Hong Kong have created a versatile uncertainty-aware AI framework broadly adaptable as a wrapper for digital pathology AI systems. (An AI wrapper acts as an interface layer that customizes, formats and automates how users interact with the underlying intelligence.) They demonstrate their wrapper, called TRUECAM, primarily with reference to non-small cell lung cancer (NSCLC) subtyping using whole-slide images.

Journal: Nature Biomedical Engineering

23-Jun-2026

Digital pathology + deep learning: Can it precisely predict lymph node metastasis risk in T1b gastric cancer?

Science China Press
Peer-Reviewed Publication

A deep learning system (DL-T1b) leveraging digital pathology achieved strong performance (AUC 0.910) for predicting lymph node metastasis in T1b gastric cancer. Integration with six clinical risk factors into a nomogram further boosted predictive accuracy, offering a precise tool for personalized treatment decisions. This study establishes proof-of-concept for deep learning-based pathomics in early gastric cancer risk stratification.

Journal: Medicine Plus