[Digitalized psychiatry : Critical considerations on a new paradigm]

Area of Science:

• Digital psychiatry
• Computational psychiatry
• Artificial intelligence in healthcare

Background:

• The integration of digitalization and artificial intelligence (AI) presents novel procedural opportunities in psychiatry.
• Current psychiatric diagnostics and treatment rely on clinical observation, which may be surpassed by data-driven methods.

Purpose of the Study:

• To examine the potential benefits and challenges of digitalized psychiatry.
• To explore the convergence of AI-driven approaches with the search for new, biologically-based disorder patterns.

Main Methods:

• Utilizing machine learning techniques.
• Leveraging big data analytics.
• Examining the transition from clinical observation to algorithmized psychiatric procedures.

Main Results:

• Machine learning and big data are expected to enable algorithmized diagnostics, prediction, and therapy with enhanced accuracy and efficiency.
• These advancements align with efforts to identify new disorder patterns beyond traditional psychopathological entities.

Conclusions:

• Digitalized psychiatry, powered by AI and big data, offers a promising future for more precise and efficient mental healthcare.
• Addressing the challenges of this digital transformation is crucial for realizing its full potential in psychiatric practice and research.