Multimodal Machine Learning in Mental Health: A Survey of Data, Algorithms, and Challenges

TL;DR

This survey reviews recent advances in multimodal machine learning for mental health, highlighting datasets, models, and challenges to guide future research and clinical applications.

Contribution

It provides the first comprehensive, clinically grounded synthesis of multimodal ML approaches, datasets, and challenges in mental health.

Findings

Cataloged 26 public multimodal datasets for mental health.

Compared 28 models using transformer, graph, and hybrid fusion strategies.

Identified key challenges like data privacy, fairness, and explainability.

Abstract

Multimodal machine learning (MML) is rapidly reshaping the way mental-health disorders are detected, characterized, and longitudinally monitored. Whereas early studies relied on isolated data streams -- such as speech, text, or wearable signals -- recent research has converged on architectures that integrate heterogeneous modalities to capture the rich, complex signatures of psychiatric conditions. This survey provides the first comprehensive, clinically grounded synthesis of MML for mental health. We (i) catalog 26 public datasets spanning audio, visual, physiological signals, and text modalities; (ii) systematically compare transformer, graph, and hybrid-based fusion strategies across 28 models, highlighting trends in representation learning and cross-modal alignment. Beyond summarizing current capabilities, we interrogate open challenges: data governance and privacy, demographic and intersectional fairness, evaluation explainability, and the complexity of mental health disorders in multimodal settings. By bridging methodological innovation with psychiatric utility, this survey aims to orient both ML researchers and mental-health practitioners toward the next generation of trustworthy, multimodal decision-support systems.