The study, published in Communications Medicine, combines magnetic resonance imaging and simulation-based artificial intelligence to speed up brain analysis while maintaining a high level of accuracy.

The method avoids the use of real patient data for training, reduces bias, and could facilitate the implementation of these advanced techniques in hospitals, helping to shorten waiting lists and improve diagnosis.

Researchers are increasingly using EEG (electroencephalography) to investigate how caffeine affects the brain during sleep — and findings suggest the issue goes beyond simply “falling asleep.” According to Prof. Donata Kurpas of Wroclaw Medical University, caffeine may reduce slow-wave activity, a key marker of deep, restorative sleep, even when sleep duration appears normal. EEG analyses reveal that the brain can remain in a more “wakeful” state overnight, potentially impairing recovery despite the subjective feeling of good sleep. Scientists also emphasize strong individual differences in caffeine sensitivity linked to genetics, metabolism, age, stress, and fatigue. The findings suggest that caffeine’s stimulating effects may contribute to a cycle of daytime fatigue and poorer nighttime recovery, highlighting the importance of sleep quality — not only sleep length — in understanding caffeine’s impact on health.

A new viewpoint article published in JMIR Mental Health argues that AI systems should evaluate the reliability of human training and feedback data to improve safety in mental health applications.

According to researchers at Semmelweis University, not only inflammation, but also sleep disorders, depression, obesity, and smoking may sustain persistent rheumatic symptoms. In their publications in the journals Nature Reviews Rheumatology and The Lancet Rheumatology, they also proposed a model that can help identify and treat the true causes of symptoms in time.