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Updated: Apr 3, 2026

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Published on: September 27, 2020

Artificial Intelligence-Empowered Multimodal Learning in Psychiatry: A Scoping Review.

Fang Li¹, Pengze Li¹, Avanti Bhandarkar¹

¹Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, Florida.

Biological Psychiatry. Cognitive Neuroscience and Neuroimaging

|April 1, 2026

Summary

This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) integrate diverse data for precision psychiatry. This approach enhances diagnosis and prognosis by analyzing complex, multimodal datasets, overcoming limitations of traditional methods.

Keywords:

Artificial intelligence Feature fusion Mental disorders Multimodal data Multimodal learning Psychiatry

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Area of Science:

Psychiatry
Artificial Intelligence
Computational Neuroscience

Background:

Psychiatric disorders exhibit high heterogeneity, challenging traditional diagnostic and prognostic accuracy.
Unimodal approaches struggle to capture the complex interplay of genetic, neurobiological, cognitive, social, and behavioral factors.
Integrating diverse data modalities is crucial for advancing precision psychiatry.

Purpose of the Study:

To systematically review the integration and analysis of multimodal data in psychiatry using advanced AI models.
To explore AI-based multimodal learning approaches and their applications in psychiatric research.
To identify challenges and future opportunities for AI-empowered precision psychiatry.

Main Methods:

Review of primary data modalities: text, neuroimaging, electrophysiology, audio/video, and multi-omics.
Analysis of AI-based multimodal learning: feature fusion, conventional ML, deep learning (DL), and Transformers.
Examination of applications in diagnosis, subtyping, stratification, risk prediction, and treatment response.

Main Results:

AI models can synthesize complex, multimodal datasets for nuanced psychiatric insights.
Applications range from current state characterization to future state prediction.
Challenges include data barriers (unpaired, limited availability, complexity) and model limitations (generalizability, interpretability).

Conclusions:

AI-empowered multimodal learning holds transformative potential for precision psychiatry.
Future opportunities lie in multimodal large language models and well-linked datasets.
Methodological innovation and interdisciplinary collaboration are key to realizing personalized diagnostics and therapeutics.