Latest AI and machine learning research in psychiatry for healthcare professionals.
The heterogeneity of brain aging is a hallmark of neurological and psychiatric disorders, yet machine-learning tools used to characterize this process, including the ‘brain age’ paradigm, have largely relied on global metrics that lack the specificity to map these complex patterns. Here, we introduce BrainAgeMap, an interpretable deep learning framework that generates fine-grained, voxel-wise maps...
We evaluated whether oxytocin improves social-emotional reciprocity in children and adolescents with autism spectrum disorder (ASD) by conducting a secondary, hypothesis-driven reanalysis of the SOARS-B trial, the largest randomized clinical trial of intranasal oxytocin to date involving over 272 youth. We used a machine learning approach to construct data-driven composite outcome measures maximal...
Repetitive transcranial magnetic stimulation (rTMS) is an established intervention for treatment-resistant depression, but response rates remain highl...
Large language models (LLMs) are rapidly entering clinical care, yet their definitionally probabilistic outputs have delivered a variety of grossly un...
Second-generation antipsychotics (SGAs) are frequently used off-label to manage behavioral symptoms in Alzheimer’s disease (AD), despite ongoing conce...
Psychiatric patients often have complex symptoms and anamneses recorded as unstructured clinical notes. Large language models (LLM) now enable large-s...
Adolescent idiopathic scoliosis (AIS) has a large impact on health-related quality of life (HRQoL) including poor psychosocial functioning, body image...
Medical jargon poses significant barriers to patient comprehension of healthcare information, potentially affecting treatment adherence and health out...
Dementia encompasses diverse clinical syndromes where diseases of the brain can manifest as impaired cognitive abilities, such as in Alzheimer’s disea...
Psychotropic medications are commonly used for children with neurodevelopmental conditions, but their effectiveness varies, making treatment selection...
This study investigates the integration of Voice AI into a locally hosted generative AI chatbot designed to function as a mental health assistant, wit...
To leverage sleep foundation models trained on large datasets of polysomnography for neurological disorder detection during an awake state. Three publ...
Postpartum depression (PPD) affects 10–15% of mothers annually, yet early identification remains challenging. We introduce ClinPreAI, a novel agentic ...
Internalizing disorders are among the most common psychiatric conditions in adolescence, often associated with long-term adverse outcomes. Early ident...
Sleep disorders pose a major global health burden and are associated with a wide range of adverse health outcomes. Polysomnography (PSG) is the gold s...
Converging neuroimaging, genetic, and post-mortem evidence highlights the fundamental role of synaptic density reductions in schizophrenia pathogenesi...
Early intervention can improve autism-related outcomes. However, no valid biosignature test exists yet for detecting or excluding autism. In addition,...
Chatbots driven by generative artificial intelligence (AI chatbots) have become ubiquitous. Recently, concerns have risen over the possibility that us...
Personalized data-driven interventions for depression are much needed. Here, we leveraged N-of-1 machine learning (ML) to optimally target behavioral ...
Medication use during adolescence provides important insight into current health and treatment patterns. However, these data are often difficult to an...