Using Machine Learning in Psychiatry: The Need to Establish a Framework That Nurtures Trustworthiness

Area of Science:

• Psychiatry
• Artificial Intelligence
• Medical Informatics

Background:

• Artificial intelligence is rapidly being adopted in psychiatry.
• Current AI applications in psychiatry lack a standardized evaluation framework.
• Methodological opacity and reviewer selection challenges hinder reliable assessment of AI studies.

Purpose of the Study:

• To highlight the urgent need for a rigorous framework to evaluate artificial intelligence in psychiatry.
• To define critical components for assessing AI methodologies in clinical settings.
• To foster trustworthiness and scientific rigor in the application of AI in mental healthcare.

Main Methods:

• Discussion of the critical issues of explainability, transparency, and generalizability in AI evaluation.
• Analysis of the definitional challenges of these terms across medicine, computer science, and law.
• Exploration of how these concepts contribute to a trustworthiness framework for AI in psychiatry.

Main Results:

• Defining explainability, transparency, and generalizability is crucial for AI trustworthiness in psychiatry.
• These terms have varied interpretations across disciplines, complicating their application.
• A clear framework is needed to ensure honest, fair, scientific, and accurate evaluation of AI methodologies.

Conclusions:

• Developing a robust evaluation framework is essential for the responsible integration of AI in psychiatry.
• Policy discussions and community diligence are necessary for reviewing clinical AI applications.
• Addressing explainability, transparency, and generalizability is key to building trust in AI-driven psychiatric tools.