Latest AI and machine learning research in psychiatry for healthcare professionals.
Spontaneous thought is pervasive in everyday human cognition, yet datasets capturing its neural dynamics under minimally interrupted conditions remain limited. The current dataset was acquired from a think-aloud functional MRI experiment in which 118 participants continuously verbalized their spontaneous thoughts during 10-minute scanning sessions. The raw MRI data and verbal transcripts with sent...
Background: Bipolar disorder (BD) is frequently underdiagnosed, particularly in patients presenting with depressive disorders, leading to delays in appropriate treatment. Artificial intelligence (AI) applied to electronic health records (EHRs) may improve early detection by identifying clinically relevant symptom patterns. Objective: To evaluate the diagnostic performance of a natural language pro...
Introduction: Adolescents with mental health disorders represent a vulnerable group with complex care needs, yet their and their relatives experiences...
Neuroectoderm-derived tissues are highly metabolically active and exhibit minimal regenerative turnover, rendering them uniquely vulnerable to age-rel...
Continuous monitoring of bipolar disorder agitation via voice biomarkers requires disentangling stable speaker traits from volatile affective states o...
Background: Most studies seeking to identify youth at increased risk for depression have developed prediction models using a limited set of risk facto...
This paper focuses on forecasting minute-by-minute stress, anxiety, and affective states using wearable sensor data. It addresses mental health as a g...
The rapid rise of large language models (LLMs) and foundation models has accelerated efforts to build artificial intelligence (AI) agents for mental h...
While 3D Gaussian splatting (3DGS) offers explicit and efficient scene representations for cone-beam computed tomography reconstruction, conventional ...
Automated depression detection often relies on static aggregation of conversational signals, potentially obscuring clinically meaningful behavioral dy...
The scarcity of high-quality annotated medical data, particularly in mental health, poses a significant bottleneck for training robust machine learnin...
BACKGROUND: Autism spectrum disorder (ASD) is marked by profound neurobiological heterogeneity, which drives inconsistent neuroimaging findings and im...
The prefrontal cortex (PFC), a brain region critical for executive and cognitive functions, is characterized by its protracted maturation extending th...
Anxiety is usually gauged by self-report, yet a single symptom level can reflect disparate neural circuitry. In Alzheimer's disease and related dement...
Background. Studies applying machine learning to obsessive-compulsive disorder (OCD) typically report accuracy in homogeneous samples but rarely asses...
Automatic depression detection from conversational interactions holds significant promise for scalable screening but remains hindered by severe data s...
The widespread adoption of social media has heightened interest in its psychological effects, particularly on mental health indicators such as anxiety...
In recent years, the integration of multimodal machine learning in wellbeing assessment has offered transformative potential for monitoring mental hea...
Emerging AI systems in behavioral health and psychiatry use multi-step or multi-agent LLM pipelines for tasks like assessing self-harm risk and screen...
Several brain foundation models (FM) have recently been proposed to predict brain disorders by modelling dynamic functional connectivity (FC). While t...