Latest AI and machine learning research in psychiatry for healthcare professionals.
Introduction: Large-scale free-text data with socio-demographic information can capture nuanced accounts of lived experience that are difficult to detect in structured measures. However, manual qualitative analysis is difficult to scale, while automated approaches may obscure subgroup variation or introduce bias. This is especially relevant for large language models (LLMs), whose use in qualitativ...
Text-based counseling is an important interface for AI mental-health support, where transcripts may be used to monitor depression severity and flag sessions requiring timely human review. However, robust PHQ-8 prediction across session regimes remains challenging: fine-tuning-based methods can exploit richer supervision but may generalize poorly under data scarcity, while prompt-based LLM methods ...
Correlation matrices are fundamental summaries of functional brain networks, yet standard analyses often treat entries independently, ignoring the cur...
Adolescence is an important developmental period during which there are diverse changes in the brain and behavior. Goal-directed behaviors and the com...
Background: Heterogeneity in symptom presentation and treatment response in irritable bowel syndrome (IBS) remains poorly understood. The gut microbio...
Background: Aberrations in neuro-immune, metabolic, and oxidative stress (NIMETOX) pathways are implicated in major depressive disorder (MDD). First-e...
Foundation models pre-trained on large neuroimaging datasets offer a promising approach to overcome the limited sample sizes typical of mental health ...
SCN2A-related disorders result from pathogenic variants in the gene encoding for the voltage-gated sodium channel Nav1.2. Collectively, these disorder...
The ability to adjust to changing environments (cognitive flexibility) and optimal decision-making are pivotal brain functions that govern successful ...
Synthetic data is widely used in healthcare to create datasets that are similar to original data but without the privacy concerns. Generating and eval...
Von Economo neurons (VENs) are selectively lost in behavioural-variant frontotemporal dementia (bvFTD) and reduced in autism spectrum conditions (ASC)...
Brain similarity networks (BSNs), extracted from structural magnetic resonance imaging, provide a validated framework for studying brain network organ...
Copy number variations (CNVs) are major structural genomic variants that contribute to a wide range of human diseases. Accurate detection of CNVs from...
Machine-learning surrogate models are positioned to help optimize deep brain stimulation (DBS) usage by predicting neural activation in response to el...
Every day, users generate digital traces (e.g., social media posts, chats, and online interactions) that are inherently timestamped and may reflect as...
Background: Mental health systems face escalating demand that exceeds clinician capacity, making accurate severity-based triage a critical bottleneck....
Post-traumatic stress disorder (PTSD) remains a significant psychiatric burden; despite growing biomarker research, no blood-based molecular diagnosti...
Human behavioral and mental health outcomes arise from interactions among genetic, environmental, and neurobiological systems. Existing frameworks oft...
Randomized neural networks (RdNNs) enable efficient, backpropagation-free training by freezing randomly initialized input-to-hidden weights, which per...
The US Food and Drug Administration (FDA) maintains a public list of artificial intelligence and machine learning (AI/ML)-enabled medical devices that...