Self-regulation is key to lowering overconfidence in Artificial Intelligence
Peer-Reviewed Publication
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.
Updates every hour. Last Updated: 26-Jun-2026 01:16 ET (26-Jun-2026 05:16 GMT/UTC)
The ESCUTIC group of the EHU-University of the Basque Country points out that overreliance on tools such as ChatGPT in learning largely depends on each student’s ability to organise him-/herself, work hard and reflect. When key skills such as perseverance, decision-making and learning from mistakes are in place, AI can become a useful aid without replacing independent thinking.
Experts propose standardized evaluation framework to support the safe integration of AI in healthcare
An international team, including researchers from the University of Innsbruck and TU Dortmund University, has once again produced a data-driven prediction for the World Cup. According to the statistical analysis, Spain is the top favorite with a 14.5 % probability, followed closely by England and France, both with 12.4 %, and Germany with 11.2 %.
A new study in Science Bulletin presents DVSTP, a deep learning system that integrates pathology images with spatial transcriptomics and proteomics to map intra-tumor heterogeneity. DVSTP predicts molecular profiles from routine pathology slides, making spatial multi-omics more accessible. Whole–tumor 3D reconstruction reveals that SRSF6 drives immune exclusion and is associated with poor clinical outcomes.
Large language models and autonomous agents have advanced rapidly, showing broad promise in medical imaging analysis, clinical diagnosis, and treatment planning. However, most existing medical AI systems still rely primarily on pre-trained knowledge and fixed workflows, making it difficult to learn continuously from long-term clinical feedback, patient outcomes, and prior treatment experience. This "static AI" architecture limits their value in complex real-world clinical settings.
To address this bottleneck, a team led by Dr. Lian Zhang from the First Hospital of Hebei Medical University, in collaboration with domestic and international research partners, has proposed VIBEMed, which is a self-evolving multi-agent framework for clinical decision support designed to enable dynamic learning and safe, traceable system evolution.
Researchers have developed tough, conductive RBA hydrogels using a hyperbranched multi-arm crosslinking strategy. The hydrogels form a dense, stable network that enables wearable sensors to monitor full-body motion and support Morse-code-based human–machine interaction.