AIMC Topic: Mental Disorders

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An open dataset and machine learning algorithms for Niacin Skin-Flushing Response based screening of psychiatric disorders.

BMC psychiatry
BACKGROUND: Niacin Skin-Flushing Response (NSR) has emerged as a promising objective biomarker for the precise diagnosis of mental disorders. However, its diagnostic potential has been constrained by the limitations of traditional statistical approac...

Ethical decision-making for AI in mental health: the Integrated Ethical Approach for Computational Psychiatry (IEACP) framework.

Psychological medicine
The integration of computational methods into psychiatry presents profound ethical challenges that extend beyond existing guidelines for AI and healthcare. While precision medicine and digital mental health tools offer transformative potential, they ...

A new framework for mental illnesses diagnosis using wearable devices aided by improved convolutional neural network.

Scientific reports
Stress inherent in the modern world is considered one of the main causes of Mental Health Disorders (MHDs) that spread in every country around the world. These mental and behavioral problems primarily affect the mind and brain that change emotions an...

Beyond the human touch: A critical review of the promise and challenges of animal- and robot-assisted therapy in loneliness and mental healthcare.

Asian journal of psychiatry
Mental health disorders, including depression, anxiety, post-traumatic stress disorder, and dementia, are increasingly recognized as exacerbated by social isolation and loneliness, prompting growing interest in Artificial Intelligence (AI) driven and...

Quantitative Research on Digitalized Treatment Options for Older Adults With Mental Illness: Scoping Review.

JMIR mental health
BACKGROUND: Older adults with mental illness face specific physical and psychosocial challenges and inequities, reflected in limited access to advanced technology. This digital divide is alarming as mental health interventions increasingly depend on ...

Comparing traditional natural language processing and large language models for mental health status classification: a multi-model evaluation.

Scientific reports
The substantial increase in mental health disorders globally necessitates scalable, accurate tools for detecting and classifying these conditions in digital environments. This study addresses the critical challenge of automated mental health classifi...

Investigating psychotherapists' attitudes towards artificial intelligence in psychotherapy.

BMC psychology
BACKGROUND: The increasing prevalence of mental health disorders, compounded by a global shortage of psychotherapists, highlights the need for innovative solutions such as Artificial Intelligence (AI) or Machine Learning (ML) applications. These tech...

The Application and Ethical Implication of Generative AI in Mental Health: Systematic Review.

JMIR mental health
BACKGROUND: Mental health disorders affect an estimated 1 in 8 individuals globally, yet traditional interventions often face barriers, such as limited accessibility, high costs, and persistent stigma. Recent advancements in generative artificial int...

γ neuromodulations: unraveling biomarkers for neurological and psychiatric disorders.

Military Medical Research
γ neuromodulation has emerged as a promising strategy for addressing neurological and psychiatric disorders, particularly in regulating executive and cognitive functions. This review explores the latest neuromodulation techniques, focusing on the cri...

AI in Medical Questionnaires: Innovations, Diagnosis, and Implications.

Journal of medical Internet research
This systematic review aimed to explore the current applications, potential benefits, and issues of artificial intelligence (AI) in medical questionnaires, focusing on its role in 3 main functions: assessment, development, and prediction. The global ...