Practical AI application in psychiatry: historical review and future directions

Abstract

The integration of artificial intelligence (AI) in mental healthcare holds promise for enhancing diagnostic precision, treatment efficacy, and personalized care. Despite AI's potential to analyze vast datasets and identify subtle patterns, its clinical adoption in psychiatry remains limited. This review critically examines the emerging role of AI in psychiatry, elucidating its utility, challenges, and implications for clinical practice. Through an extensive analysis of the existing literature and empirical evidence, we seek to inform psychiatric stakeholders about both opportunities and obstacles that are presented by AI. We evaluate AI's potential to improve diagnostic accuracy, prognostic performance, and therapeutic interventions. Our pragmatic approach bridges the gap between theoretical advancements and practical implementation, providing valuable insights and actionable recommendations for psychiatric professionals. This article highlights the supportive role of AI, advocating for its judicious integration to enhance patient outcomes while maintaining the human-centric essence of psychiatric practice. By addressing these challenges and fostering collaboration, AI can significantly advance mental healthcare, reduce clinical burdens, and improve patient outcomes.

Fig. 1. Artificial intelligence model application in psychiatry.

Data flow from multimodal sources through preprocessing and AI-based modeling using a transformer architecture. The model consists of input embedding, encoding, and decoding stages utilizing multi-head attention and feed-forward layers, residual layer normalization, and a linear output layer. Patient feedback informs model optimization and updates, and pattern recognition and prediction support clinical practice through diagnostic support, treatment recommendations, prognostic prediction, and ongoing monitoring.

Fig. 2

Synergy between clinical expertise and analytical capabilities of AI for optimized patient care.

Fig. 3. From traditional statistical methods to machine learning in psychiatry.

This figure illustrates the shift from traditional statistical methods to advanced machine learning techniques in identifying different treatment benefits in psychiatry. Traditional statistical approaches typically yield results that reflect average treatment effects across a population, which may not accurately represent actual treatment effects in individual patients with heterogeneous characteristics. These methods, which test variations in treatment effects across individual patient characteristics using statistical significance cutoffs, are susceptible to false discoveries and false-negative results. In contrast, causal machine learning methods provide robust alternatives that are capable of more effectively identifying heterogeneous treatment effects. These methods offer a granular understanding of when treatments are beneficial or harmful, thereby enabling personalized decision-making in patient care that is tailored to individual patient profiles.

Fig. 4. Artificial intelligence-driven digital tools, leveraging internet-connected devices and machine learning advancements, offer transformative opportunities to promote mental health in the digital age.

ADHD attention-deficit/hyperactivity disorder, SVM support vector machine, GMM Gaussian Mixture Model.