Latest AI and machine learning research in psychiatry for healthcare professionals.
Aperiodic neural activity has been the subject of intense research interest lately as it could reflect on the cortical excitation/inhibition ratio, which is suspected to be affected in numerous clinical conditions. This phenomenon is characterized via the aperiodic scaling exponent $\beta$, equal to the spectral slope following log-log transformation of power spectra. Despite recent progress, ho...
BACKGROUND: The incidence of cardiovascular metabolic diseases (CMD) continues to rise among middle-aged and elderly populations, affecting not only physical health but also significantly increasing the risk of depression. This study aims to construct a machine learning model to predict the risk of depression in middle-aged and elderly patients with CMD and to identssify key risk factors.
BACKGROUND: General awareness and exposure to generative artificial intelligence (AI) have increased recently. This transformative technology has the ...
Artificial Intelligence (AI) holds promise for addressing significant challenges in mental healthcare, such as workforce shortages, waiting lists, and...
Studies on schizophrenia assessments using deep learning typically treat it as a classification task to detect the presence or absence of the disord...
College students are increasingly affected by stress, anxiety, and depression, yet face barriers to traditional mental health care. This study evalu...
Large language models (LLMs) hold significant potential for mental health support, capable of generating empathetic responses and simulating therape...
The increasing prevalence of mental health disorders globally highlights the urgent need for effective digital screening methods that can be used in...
Gaining insights into the structural and functional mechanisms of the brain has been a longstanding focus in neuroscience research, particularly in ...
Clinical studies reveal disruptions in brain structural connectivity (SC) and functional connectivity (FC) in neuropsychiatric disorders such as sch...
The proliferation of Large Language Models (LLMs) and Intelligent Virtual Agents acting as psychotherapists presents significant opportunities for e...
Can small language models with 0.5B to 5B parameters meaningfully engage in trauma-informed, empathetic dialogue for individuals with PTSD? We addre...
When addressing complex questions that require new information, people often associate the question with existing knowledge to derive a sensible ans...
Novel psychoactive substances (NPS) pose one of the greatest challenges across the illicit drug landscape. They can be highly potent, and coupled with...
Emotion monitoring plays a crucial role in mental health management. However, traditional methods of emotion recognition predominantly rely on subject...
Understanding people's preferences and needs is crucial for urban planning decisions, yet current approaches often combine them from multi-cultural ...
Heightened negative affect is a core feature of serious mental illness. Over 90% of American adults own a smartphone, equipped with an array of sensor...
Identifying likely placebo responders can help design more efficient clinical trials by stratifying participants, reducing sample size requirements, a...
Large language models (LLMs) increasingly power mental-health chatbots, yet the field still lacks a scalable, theory-grounded way to decide which mo...
Suicide remains one of the main preventable causes of death among active service members and veterans. Early detection and prediction are crucial in...