Psychiatry

[Brave New Psychiatry? Or: What Astray is Artificial Intelligence Leading Psychiatry?].

The article summarizes various publications on the application of "learning algorithms" and "artificial neural networks" in psychiatry to describe a dystopian future scenario. The draft of a nosology based on molecular biology is opposed to the ecological disturbance concept of psychiatry developed from the critical examination of history and in dialogue with the stakeholders.

[Psychotherapy with an Autonomous Artifical Intelligence - Ethical Benefits and Challenges].

OBJECTIVE: This paper provides an overview of a range of ethical aspects involved in the use of autonomous, virtual or embodied artificial intelligence (AI) in the care of people with mental health issues.

[Comments on Relationships with Artificial Emotional Intelligence - from "Here and Now" to "There and Then"].

The structure of relationships in the past, the present and the future is shaped by the idea of humanism. Based on this construct, the article illumin...

Machine learning and bioinformatic analysis of brain and blood mRNA profiles in major depressive disorder: A case-control study.

This study analyzed gene expression messenger RNA data, from cases with major depressive disorder (MDD) and controls, using supervised machine learnin...

Machine Learning Assessment of Early Life Factors Predicting Suicide Attempt in Adolescence or Young Adulthood.

IMPORTANCE: Although longitudinal studies have reported associations between early life factors (ie, in-utero/perinatal/infancy) and long-term suicida...

Protein glycation - biomarkers of metabolic dysfunction and early-stage decline in health in the era of precision medicine.

Protein glycation provides a biomarker in widespread clinical use, glycated hemoglobin HbA (A1C). It is a biomarker for diagnosis of diabetes and pred...

Deep learning based automatic diagnosis of first-episode psychosis, bipolar disorder and healthy controls.

Neuroimaging data driven machine learning based predictive modeling and pattern recognition has been attracted strongly attention in biomedical scienc...

Predicting the 9-year course of mood and anxiety disorders with automated machine learning: A comparison between auto-sklearn, naïve Bayes classifier, and traditional logistic regression.

BACKGROUND: Predicting the onset and course of mood and anxiety disorders is of clinical importance but remains difficult. We compared the predictive ...

Patients' Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment.

BACKGROUND: Misdiagnosis, arbitrary charges, annoying queues, and clinic waiting times among others are long-standing phenomena in the medical industr...

Convolutional Neural Network-Based Deep Learning Model for Predicting Differential Suicidality in Depressive Patients Using Brain Generalized q-Sampling Imaging.

OBJECTIVE: Suicide is a priority health problem. Suicide assessment depends on imperfect clinician assessment with minimal ability to predict the risk...

Patient journey through cases of depression from claims database using machine learning algorithms.

Health insurance and acute hospital-based claims have recently become available as real-world data after marketing in Japan and, thus, classification ...

Paradoxical sensory reactivity induced by functional disconnection in a robot model of neurodevelopmental disorder.

Neurodevelopmental disorders are characterized by heterogeneous and non-specific nature of their clinical symptoms. In particular, hyper- and hypo-rea...

Whole-brain modelling of resting state fMRI differentiates ADHD subtypes and facilitates stratified neuro-stimulation therapy.

Recent advances in non-linear computational and dynamical modelling have opened up the possibility to parametrize dynamic neural mechanisms that drive...

Resting-state functional network models for posttraumatic stress disorder.

Four recent articles were examined for their use of resting-state functional magnetic resonance imaging on participants with posttraumatic symptoms. T...

Deep learning applications for the classification of psychiatric disorders using neuroimaging data: Systematic review and meta-analysis.

Deep learning (DL) methods have been increasingly applied to neuroimaging data to identify patients with psychiatric and neurological disorders. This ...

Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom.

Artificial intelligence (AI) is increasingly employed in health care fields such as oncology, radiology, and dermatology. However, the use of AI in me...

Barriers and facilitators to the implementation of social robots for older adults and people with dementia: a scoping review protocol.

BACKGROUND: Psychosocial health issues such as depression and social isolation are an important cause of morbidity and premature mortality for older a...

Pattern classification as decision support tool in antipsychotic treatment algorithms.

Pattern classification aims to establish a new approach in personalized treatment. The scope is to tailor treatment on individual characteristics duri...

Using weak supervision and deep learning to classify clinical notes for identification of current suicidal ideation.

Mental health concerns, such as suicidal thoughts, are frequently documented by providers in clinical notes, as opposed to structured coded data. In t...

Use of a tele-operated robot to increase sociability in individuals with autism spectrum disorder who display Hikikomori.