Psychiatry

Latest AI and machine learning research in psychiatry for healthcare professionals.

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Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation

Depression is a widespread mental health disorder, and clinical interviews are the gold standard for assessment. However, their reliance on scarce professionals highlights the need for automated detection. Current systems mainly employ black-box neural networks, which lack interpretability, which is crucial in mental health contexts. Some attempts to improve interpretability use post-hoc LLM gen...

From statistics to deep learning: Using large language models in psychiatric research.

BACKGROUND: Large Language Models (LLMs) hold promise in enhancing psychiatric research efficiency. However, concerns related to bias, computational demands, data privacy, and the reliability of LLM-generated content pose challenges. GAP: Existing studies primarily focus on the clinical applications of LLMs, with limited exploration of their potentials in broader psychiatric research.

Mar 1 2025 39777756
NNFit: A Self-Supervised Deep Learning Method for Accelerated Quantification of High-Resolution Short-Echo-Time MR Spectroscopy Datasets.

Purpose To develop and evaluate the performance of NNFit, a self-supervised deep learning method for quantification of high-resolution short-echo-time...

Mar 1 2025 39812584
Autism Spectrum Disorder Detection Using Prominent Connectivity Features from Electroencephalography.

Autism Spectrum Disorder (ASD) is a disorder of brain growth with great variability whose clinical presentation initially shows up during early stages...

Mar 1 2025 39962835
Virtual reality and artificial intelligence: the future of mental health. A narrative review.

In recent years, the use of artificial intelligence (AI) and virtual reality (VR) in the psychiatric field has been rapidly developing. This narrative...

Mar 1 2025 40084445
Artificial Intelligence and the Future of Psychotherapy: A Medical Student Perspective.

This article explores the ways artificial intelligence (AI) may impact the field of psychotherapy through the perspective of a prospective psychiatric...

Mar 1 2025 40094879
A glance into the future of artificial intelligence-enhanced scalable personalized training: A response to Kopelovich, Brian, et al. (2025) and Kopelovich, Slevin, et al. (2025).

The two articles by Kopelovich, Brian, et al. (2025) and Kopelovich, Slevin, et al. (2025) mark a new era in psychotherapy research and practice. The ...

Mar 1 2025 40095966
PsychBench: A comprehensive and professional benchmark for evaluating the performance of LLM-assisted psychiatric clinical practice

The advent of Large Language Models (LLMs) offers potential solutions to address problems such as shortage of medical resources and low diagnostic c...

Hypergraph Multi-Modal Learning for EEG-based Emotion Recognition in Conversation

Emotional Recognition in Conversation (ERC) is an important method for diagnosing health conditions such as autism or depression, as well as underst...

ProAI: Proactive Multi-Agent Conversational AI with Structured Knowledge Base for Psychiatric Diagnosis

Most LLM-driven conversational AI systems operate reactively, responding to user prompts without guiding the interaction. Most LLM-driven conversati...

FedMentalCare: Towards Privacy-Preserving Fine-Tuned LLMs to Analyze Mental Health Status Using Federated Learning Framework

With the increasing prevalence of mental health conditions worldwide, AI-powered chatbots and conversational agents have emerged as accessible tools...

Mixture of Experts for Recognizing Depression from Interview and Reading Tasks

Depression is a mental disorder and can cause a variety of symptoms, including psychological, physical, and social. Speech has been proved an object...

DreamNet: A Multimodal Framework for Semantic and Emotional Analysis of Sleep Narratives

Dream narratives provide a unique window into human cognition and emotion, yet their systematic analysis using artificial intelligence has been unde...

Efficient 4D fMRI ASD Classification using Spatial-Temporal-Omics-based Learning Framework

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder impacting social and behavioral development. Resting-state fMRI, a non-invasive tool...

Evidence-Driven Marker Extraction for Social Media Suicide Risk Detection

Early detection of suicide risk from social media text is crucial for timely intervention. While Large Language Models (LLMs) offer promising capabi...

NeuroTree: Hierarchical Functional Brain Pathway Decoding for Mental Health Disorders

Mental disorders are among the most widespread diseases globally. Analyzing functional brain networks through functional magnetic resonance imaging ...

Uncertainty Modeling in Multimodal Speech Analysis Across the Psychosis Spectrum

Capturing subtle speech disruptions across the psychosis spectrum is challenging because of the inherent variability in speech patterns. This variab...

Uncertainty-aware abstention in medical diagnosis based on medical texts

This study addresses the critical issue of reliability for AI-assisted medical diagnosis. We focus on the selection prediction approach that allows ...

[The joint analysis of heart health and mental health based on continual learning].

Cardiovascular diseases and psychological disorders represent two major threats to human physical and mental health. Research on electrocardiogram (EC...

Feb 25 2025 40000169
Moving Beyond Medical Exam Questions: A Clinician-Annotated Dataset of Real-World Tasks and Ambiguity in Mental Healthcare

Current medical language model (LM) benchmarks often over-simplify the complexities of day-to-day clinical practice tasks and instead rely on evalua...

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