Artificial Intelligence in Psychiatry: A Review of Biological and Behavioral Data Analyses DOI Creative Commons
Ismail BAYDİLİ, Burak Taşçı, Gülay TAŞCI

et al.

Diagnostics, Journal Year: 2025, Volume and Issue: 15(4), P. 434 - 434

Published: Feb. 11, 2025

Artificial intelligence (AI) has emerged as a transformative force in psychiatry, improving diagnostic precision, treatment personalization, and early intervention through advanced data analysis techniques. This review explores recent advancements AI applications within focusing on EEG ECG analysis, speech natural language processing (NLP), blood biomarker integration, social media utilization. EEG-based models have significantly enhanced the detection of disorders such depression schizophrenia spectral connectivity analyses. ECG-based approaches provided insights into emotional regulation stress-related conditions using heart rate variability. Speech frameworks, leveraging large (LLMs), improved cognitive impairments psychiatric symptoms nuanced linguistic feature extraction. Meanwhile, analyses deepened our understanding molecular underpinnings mental health disorders, analytics demonstrated potential for real-time surveillance. Despite these advancements, challenges heterogeneity, interpretability, ethical considerations remain barriers to widespread clinical adoption. Future research must prioritize development explainable models, regulatory compliance, integration diverse datasets maximize impact care.

Language: Английский

Artificial Intelligence in Psychiatry: A Review of Biological and Behavioral Data Analyses DOI Creative Commons
Ismail BAYDİLİ, Burak Taşçı, Gülay TAŞCI

et al.

Diagnostics, Journal Year: 2025, Volume and Issue: 15(4), P. 434 - 434

Published: Feb. 11, 2025

Artificial intelligence (AI) has emerged as a transformative force in psychiatry, improving diagnostic precision, treatment personalization, and early intervention through advanced data analysis techniques. This review explores recent advancements AI applications within focusing on EEG ECG analysis, speech natural language processing (NLP), blood biomarker integration, social media utilization. EEG-based models have significantly enhanced the detection of disorders such depression schizophrenia spectral connectivity analyses. ECG-based approaches provided insights into emotional regulation stress-related conditions using heart rate variability. Speech frameworks, leveraging large (LLMs), improved cognitive impairments psychiatric symptoms nuanced linguistic feature extraction. Meanwhile, analyses deepened our understanding molecular underpinnings mental health disorders, analytics demonstrated potential for real-time surveillance. Despite these advancements, challenges heterogeneity, interpretability, ethical considerations remain barriers to widespread clinical adoption. Future research must prioritize development explainable models, regulatory compliance, integration diverse datasets maximize impact care.

Language: Английский

Citations

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