Psychiatry

Schizophrenia

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

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Evaluation of large language model chatbot responses to psychotic prompts

The large language model (LLM) chatbot product ChatGPT has accumulated 800 million weekly users sinc...

TRUSTING: An International Multicenter Observational Study of Speech-Based Relapse Prediction in Psychosis Using Explainable AI

The course of psychotic disorders typically involves relapses. Early warning signs vary between indi...

Deconstructing Cognitive Impairment in Psychosis With a Machine Learning Approach.

IMPORTANCE: Cognitive functioning is associated with various factors, such as age, sex, education, a...

Jan 2025 39382875
Diagnosis of Schizophrenia and Its Subtypes Using MRI and Machine Learning.

PURPOSE: The neurobiological heterogeneity present in schizophrenia remains poorly understood. This ...

Jan 2025 39740776
Semantic abnormalities in schizophrenia and bipolar disorder: A natural language processing approach.

INTRODUCTION: The diagnostic boundaries between schizophrenia and bipolar disorder are controversial...

(1)
Jan 2025 39846293
Towards a Systematic Evaluation of Hallucinations in Large-Vision Language Models

Large Vision-Language Models (LVLMs) have demonstrated remarkable performance in complex multimoda...

Is Your Text-to-Image Model Robust to Caption Noise?

In text-to-image (T2I) generation, a prevalent training technique involves utilizing Vision Langua...

(1)
MedHallBench: A New Benchmark for Assessing Hallucination in Medical Large Language Models

Medical Large Language Models (MLLMs) have demonstrated potential in healthcare applications, yet ...

Extract Free Dense Misalignment from CLIP

Recent vision-language foundation models still frequently produce outputs misaligned with their in...

Multimodal Preference Data Synthetic Alignment with Reward Model

Multimodal large language models (MLLMs) have significantly advanced tasks like caption generation...

Token Preference Optimization with Self-Calibrated Visual-Anchored Rewards for Hallucination Mitigation

Direct Preference Optimization (DPO) has been demonstrated to be highly effective in mitigating ha...

ReXTrust: A Model for Fine-Grained Hallucination Detection in AI-Generated Radiology Reports

The increasing adoption of AI-generated radiology reports necessitates robust methods for detectin...

RAC3: Retrieval-Augmented Corner Case Comprehension for Autonomous Driving with Vision-Language Models

Understanding and addressing corner cases is essential for ensuring the safety and reliability of ...

Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models

While large vision-language models (LVLMs) have shown impressive capabilities in generating plausi...

Advancements in Machine Learning and Deep Learning for Early Detection and Management of Mental Health Disorder

For the early identification, diagnosis, and treatment of mental health illnesses, the integration...

Evaluating Hallucination in Text-to-Image Diffusion Models with Scene-Graph based Question-Answering Agent

Contemporary Text-to-Image (T2I) models frequently depend on qualitative human evaluations to asse...

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Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis

Recent advancements in large vision-language models (LVLM) have significantly enhanced their abili...

(1)
An Evolutionary Large Language Model for Hallucination Mitigation

The emergence of LLMs, like ChatGPT and Gemini, has marked the modern era of artificial intelligen...

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