BACKGROUND: The integration of Artificial Intelligence (AI) in psychiatry presents opportunities for enhancing patient care but raises significant ethical concerns and challenges in clinical application. Addressing these challenges necessitates an in...
American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics
39031613
Psychiatric disorders have a complex biological underpinning likely involving an interplay of genetic and environmental risk contributions. Substantial efforts are being made to use artificial intelligence approaches to integrate features within and ...
The perspective of personalized medicine for brain disorders requires efficient learning models for anatomical neuroimaging-based prediction of clinical conditions. There is now a consensus on the benefit of deep learning (DL) in addressing many medi...
INTRODUCTION: Medical decision-making is crucial for effective treatment, especially in psychiatry where diagnosis often relies on subjective patient reports and a lack of high-specificity symptoms. Artificial intelligence (AI), particularly Large La...
This study aimed to assess the ability of an artificial intelligence (AI)-based chatbot to generate abstracts from academic psychiatric articles. We provided 30 full-text psychiatric papers to ChatPDF (based on ChatGPT) and prompted generating a simi...
European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology
39232341
To understand the complex nature of heterogeneous psychiatric disorders, scientists and clinicians are required to employ a wide range of clinical, endophenotypic, neuroimaging, genomic, and environmental data to understand the biological mechanisms ...
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...
For many years, it has been widely accepted in the psychiatric field that clinical practice cannot be reduced to finely tuned statistical prediction systems utilizing diverse clinical data. Clinicians are recognized for their unique and irreplaceable...