Latest AI and machine learning research in psychiatry for healthcare professionals.
Background: Electroconvulsive therapy (ECT) induces widespread brain effects and remains the most effective intervention for severe major depressive disorder (MDD). However, how ECT reshapes the global organization of functional connectomes remains poorly understood. Edge-centric connectomics offers a framework for characterizing large-scale reconfiguration beyond conventional node-based analyses....
Background: Depression and anxiety are managed largely between clinical visits, yet outpatient care lacks scalable, accountable mechanisms for between-visit support. Large language models converse fluently but fuse clinical reasoning with language generation in one opaque process, so they cannot reliably deliver evidence-based psychotherapy and typically operate outside clinician oversight. Object...
Genetic prediction of complex phenotypes typically relies on additive linear models, which scale well but cannot capture non-additive effects or deepl...
Digital phenotyping (DP) using smartphones and wearable devices has shown considerable potential for mental health monitoring. However, progress remai...
Machine learning approaches may support individual-level classification in psychiatry, but many EEG-based schizophrenia studies have relied on small s...
Electromyography (EMG) is fundamental to clinical assessment, rehabilitation, neuromuscular research, and human-machine interfaces. Despite decades of...
Synthetic data augmentation in speech is common practice for linguistic tasks like ASR, but has seen far less work for paralinguistic ones, especially...
Purpose Several clinical studies have shown correlations between certain physiological measure-ments and an ASD diagnosis. Such findings, however, hav...
Background: Cognitive behavioural therapy (CBT) is the most frequently used and recommended therapy program for mental health conditions, including fo...
Background: Emotion dysregulation is a core feature of bipolar disorder (BD), yet its behavioral expression during depressive episodes, and potential ...
Postpartum depression (PPD) is a serious perinatal mental health condition affecting approximately 20% of new mothers worldwide. Common screening appr...
Deep brain stimulation (DBS) is effective for treatment-refractory obsessive-compulsive disorder (OCD), but outcomes are heterogeneous and non-respond...
Large Language Models are increasingly used in consumer-facing mental health tools, many of which claim that prompt engineering alone can ensure safe ...
Background. UK clinical guidance recommends that structured risk prediction tools and risk stratification should not be used in self-harm, to predict ...
The increased vulnerability to mental health problems in adolescence is frequently reported but poorly understood, hampered by a rigid diagnostic syst...
Abstract Background Despite a rising global psychiatric burden, a treatment gap persists where the majority of symptomatic individuals remain unmedica...
Background General-purpose large language models increasingly encounter emotional and therapy-like conversation, yet are not developed or evaluated as...
Explainable machine learning (XML) pipelines applied to composite mental health outcomes can produce apparently-robust, cross-population-stable risk h...
Precision medicine faces a critical challenge in translating high-dimensional omics data into robust disease predictions across diverse populations. C...
Autism development involves multiple genetic and early-life environmental factors. Studying the placenta's gene expression profile may reveal key mech...