Latest AI and machine learning research in psychiatry for healthcare professionals.
The integration of chatbots into psychiatry introduces a novel approach to support clinical decision-making, but biases in their recommendations pose significant concerns. This study investigates potential biases in chatbot-generated recommendations for adjunctive therapy in difficult-to-treat depression, comparing these outputs with the Canadian Network for Mood and Anxiety Treatments (CANMAT) 20...
Autism Spectrum Disorder (ASD) is a prevalent neurodevelopmental condition influenced by both genetic and environmental factors, including pesticide exposure. This study aims to investigate the pathogenic mechanisms of ASD and identify potential causative pesticides by integrating bioinformatics, machine learning, network toxicology, and molecular docking approaches. A total of 156 differentially ...
Constrained by the cost and ethical concerns of involving real seekers in AI-driven mental health, researchers develop LLM-based conversational agen...
Identifying evolutionary correspondences between cell types across species is a fundamental challenge in comparative genomics and evolutionary biolo...
The Facial Action Coding System (FACS) has been used by numerous studies to investigate the links between facial behavior and mental health. The lab...
BACKGROUND: Artificial intelligence (AI) is increasingly used in healthcare interventions to provide accessible, continuous, and personalized patient ...
BACKGROUND: Conventional approaches for major depressive disorder (MDD) screening rely on two effective but subjective paradigms: self-rated scales an...
3D Gaussian Splatting (3DGS) marks a significant milestone in balancing the quality and efficiency of differentiable rendering. However, its high ef...
Group conversations are valuable for second language (L2) learners as they provide opportunities to practice listening and speaking, exercise comple...
Speech is a noninvasive digital phenotype that can offer valuable insights into mental health conditions, but it is often treated as a single modali...
Mental disorders including depression, anxiety, and other neurological disorders pose a significant global challenge, particularly among individuals...
BACKGROUND: There is a lack of studies exploring the performance of Transformers-based language models in common risks assessment among psychiatric in...
Despite rapid advances in large language models (LLMs), their integration with traditional supervised machine learning (ML) techniques that have pro...
Mental manipulation is a subtle yet pervasive form of abuse in interpersonal communication, making its detection critical for safeguarding potential...
Large language models (LLMs) are increasingly used in decision-making tasks like r\'esum\'e screening and content moderation, giving them the power ...
Limited access to mental healthcare, extended wait times, and increasing capabilities of Large Language Models (LLMs) has led individuals to turn to...
The detection of mental health problems from social media and the interpretation of these results have been extensively explored. Research has shown...
Current care in multiple sclerosis (MS) primarily relies on infrequently obtained data such as magnetic resonance imaging, clinical laboratory tests o...
Parkinson's Disease (PD) is a neurodegenerative disorder that significantly impacts motor and non-motor functions. There is currently no treatment t...
Aperiodic neural activity has been the subject of intense research interest lately as it could reflect on the cortical excitation/inhibition ratio, ...