INTRODUCTION: Mental disorders, such as anxiety and depression, significantly impacted global populations in 2019 and 2020, with COVID-19 causing a surge in prevalence. They affect 13.4% of the people worldwide, and 21% of Iranians have experienced t...
BMC medical informatics and decision making
39578814
BACKGROUND: There are numerous papers focusing on diagnosing mental health disorders using unimodal and multimodal approaches. However, our literature review shows that the majority of these studies either use unimodal approaches to diagnose a variet...
BACKGROUND: Mental health disorders are currently the main contributor to poor quality of life and years lived with disability. Symptoms common to many mental health disorders lead to impairments or changes in the use of language, which are observabl...
BACKGROUND: Assessing the complex and multifaceted symptoms of patients with acute psychiatric disorders proves to be significantly challenging for clinicians. Moreover, the staff in acute psychiatric wards face high work intensity and risk of burnou...
OBJECTIVES: Tools based on generative artificial intelligence (AI) such as ChatGPT have the potential to transform modern society, including the field of medicine. Due to the prominent role of language in psychiatry, e.g., for diagnostic assessment a...
PURPOSE OF REVIEW: This review aims to evaluate the current psychiatric applications and limitations of machine learning (ML), defined as techniques used to train algorithms to improve performance at a task based on data. The review emphasizes the cl...
BACKGROUND: This study investigated socio-demographic, psychiatric, and psychological characteristics of patients with high versus low utilization of psychiatric inpatient services. Our objective was to better understand the utilization pattern and t...
Psychiatric disorders are influenced by genetic and environmental factors. However, their study is hindered by limitations on precisely characterizing human behavior. New technologies such as wearable sensors show promise in surmounting these limitat...
BACKGROUND: Machine Learning (ML) models have been used to predict common mental disorders (CMDs) and may provide insights into the key modifiable factors that can identify and predict CMD risk and be targeted through interventions. This systematic r...
OBJECTIVE: This paper investigates how state-of-the-art generative artificial intelligence (AI) image models represent common psychiatric diagnoses. We offer key lessons derived from these representations to inform clinicians, researchers, generative...