Latest AI and machine learning research in psychiatry for healthcare professionals.
Artificial intelligence (AI) has been increasingly integrated into autism interventions to support personalization and scalability; however, the strength of empirical evidence supporting these approaches remains unclear. In this systematic review, we synthesized and critically appraised experimental studies evaluating AI-based interventions for autistic individuals with a specific focus on interve...
BACKGROUND/AIMS: : Distal esophageal spasm (DES) is a rare, heterogeneous esophageal motility disorder with variable treatment responses. Machine learning methods are well suited to distinguish DES phenotypes that could inform therapeutic decisions and outcomes. This exploratory study aims to identify and characterize unique DES phenotypes using unsupervised machine learning clustering. METHODS: :...
BACKGROUND: The cooccurrence of posttraumatic stress disorder (PTSD) and opioid use heightens suicide risk. We aimed to develop and validate a machine...
BACKGROUND: Artificial Intelligence (AI) has emerged as a transformative force revolutionizing various sectors, including healthcare, particularly the...
BACKGROUND: Obesity is a well-established risk factor for major depressive disorder (MDD), yet the risk is not uniform, highlighting the need for prec...
Aim: We aimed to compare Quan and colleagues (2011) established weights for the Charlson Comorbidity Index (CCI) conditions to autism-specific weights...
Psychiatric symptoms in Parkinson's disease (PD) are highly prevalent and challenging to treat. This study maps oscillatory neural activity to diverse...
BACKGROUND: Artificial intelligence (AI) is conquering medicine in many fields. With geriatric patients, it is important not only to understand the de...
BACKGROUND: Agriculture is widely acknowledged as a dangerous industry for workers. Agriculture workers are also older compared with other industries,...
Rapid AI integration has introduced novel psychosocial stressors. Little is known about AI-specific clinical impacts in resource-limited settings. Thi...
BACKGROUND: As digital technologies become increasingly embedded in daily life, their roles in mental health care have expanded and diversified. Digit...
BACKGROUND: Identifying traits of narcissistic personality disorder (NPD) is clinically challenging, yet early detection can significantly improve out...
Major depressive disorder (MDD) is a serious mental health disorder that is understood to affect an individual's speech signals, including observed va...
State-level regulation of AI used for mental health is emerging in the absence of a federal framework. States are taking different approaches to regul...
BACKGROUND: By 2050, 22% of the global population will be aged 60 years or older, with Europe experiencing rapid aging. This increase in chronic disea...
Understanding gene regulation at single-cell resolution is crucial for unraveling development, disease, and cellular identity. We introduce single-cel...
BACKGROUND: Pharmacotherapy for common mental disorders is frequently limited by adverse events and suboptimal adherence. While music therapy offers a...
Autism Spectrum Disorder (ASD) is a highly heterogeneous neurodevelopmental condition characterized by significant inter-subject variability in electr...
OBJECTIVES: To map the available evidence on the use of health databases for the early identification of autism spectrum disorder (ASD) across diverse...
OBJECTIVE: Psychiatry graduate medical education (GME) faces converging pressures of increasing clinical demand, rising administrative burden, and wor...