Precision Psychiatry for Obsessive-Compulsive Disorder: Clinical Applications of Deep Learning Architectures

Journal of Clinical Medicine, Год журнала: 2025, Номер 14(7), С. 2442 - 2442

Опубликована: Апрель 3, 2025

Obsessive-compulsive disorder (OCD) is a complex psychiatric condition characterized by significant heterogeneity in symptomatology and treatment response. Advances neuroimaging, EEG, other multimodal datasets have created opportunities to identify biomarkers predict outcomes, yet traditional statistical methods often fall short analyzing such high-dimensional data. Deep learning (DL) offers powerful tools for addressing these challenges leveraging architectures capable of classification, prediction, data generation. This brief review provides an overview five key DL architectures-feedforward neural networks, convolutional recurrent generative adversarial transformers-and their applications OCD research clinical practice. We highlight how models been used the predictors response, diagnose classify OCD, advance precision psychiatry. conclude discussing implementation DL, summarizing its advances promises underscoring field.

Язык: Английский

Harnessing Large Language Models for Identification and Treatment of Obsessive-Compulsive Disorder

Опубликована: Июнь 13, 2024

Obsessive-Compulsive Disorder (OCD) is a mental health condition marked by recurrent intrusive thoughts or sensations that compel individuals to perform repetitive behaviors acts. Obsessions and compulsions significantly disrupt daily life cause considerable distress. Early identification intervention improve long-term outcomes. This study aimed evaluate the ability of four advanced artificial intelligence models (ChatGPT-3.5, ChatGPT-4, Claude, Bard) accurately recognize OCD compared human professionals assess recommended therapies stigma attributions. was conducted during March 2024 utilizing 12 vi-gnettes. Each vignette depicted client, either young adult middle-aged male female, attending an initial therapy session. evaluated ten times, resulting in 480 evaluations. The results were with those sample 514 psychotherapists, as reported Canavan. Significant differences found. AI demonstrated higher recognition rates confidence levels than showed 100% recognition, 87% among psychotherapists. also evi-dence-based interventions more frequently, ChatGPT-3.5 Claude at 100%, ChatGPT-4 90%, Bard 60%, 61.9% Additionally, ex-hibited lower danger estimations, though both psychotherapists high willingness treat described cases. findings suggest surpass recognizing recommending evidence-based treatments while demonstrating stigma. These highlight potential tools enhance diagnosis treatment clinical settings.

Язык: Английский