TL;DR: A 2026 study in Nature Mental Health describes an AI system that scans routine electronic health records (EHRs) and combines early-life visits, prescriptions, and comorbidities into a risk flag for ADHD evaluation before formal diagnosis. Key Findings AI flagged ADHD risk from routine EHR data alone: The model identified patterns predictive of future ADHD …
AI Brain Biomarkers Shift When Models Pick Different Features TL;DR: Across more than 12,000 participants and 13 outcomes, overlooked brain-connectivity features predicted cognition and psychiatric traits nearly as well as top-ranked features, but pointed to different underlying circuits. Key Findings Hidden features held predictive power: The authors tested their idea across HBN, ABCD, HCPD, and …
A new study uses AutoML and blood DNA methylation data to identify novel gene biomarkers for diagnosing schizophrenia (SCZ) with high accuracy. Highlights: Biomarker Discovery: AutoML identified three schizophrenia-specific gene methylation biomarkers: IGF2BP1, CENPI, and PSME4. Methylation Analysis: IGF2BP1 showed higher methylation and PSME4 showed lower methylation in SCZ patients compared to healthy controls, while …
A 2016 study exploring whether machine learning can accurately predict criminality based solely on facial images has sparked debate on the social implications of such technology. The research, conducted by Chinese academics Xiaolin Wu and Xi Zhang, claims to be the first of its kind in using automated face recognition to categorize criminals versus non-criminals. …
Depression is a growing mental health concern, especially among young adults. New AI-powered research offers a rapid, objective screening tool to identify those at high risk using eye tracking technology. This innovative approach could revolutionize early detection and intervention. Key Facts: Depression is the leading cause of disability worldwide, often starting in young adulthood. Early …
Research provides new evidence that generalized anxiety disorder (GAD) and depressive disorder (DD) have distinct patterns of brain activity. Machine learning analysis of EEG data reveals signature brain features that may enable more accurate diagnosis. Key Facts: DD showed increased beta band power and complexity compared to GAD Brain network connectivity was altered in both …
Scientists have developed a new way to identify hit songs that people will love by measuring listeners’ brain activity responses using machine learning AI. This approach was much more accurate than asking people if they liked each song. Key Facts: Researchers measured people’s brain activity while they listened to 24 new songs. They tracked emotional …
Researchers have reconstructed a recognizable version of the Pink Floyd song “Another Brick in the Wall” directly from recorded brain activity. Using advanced machine learning techniques, the team was able to extract enough acoustic information from listeners’ brain signals to identify the song and recreate an intelligible version. Key highlights: Researchers recorded brain activity via …
