Generative multimodal large language models in mental health care: Applications, opportunities, and challenges

TL;DR

This paper reviews how multimodal large language models can enhance mental health care by integrating diverse data types beyond text.

Contribution

The paper introduces the novel application of multimodal LLMs in mental health care, emphasizing their potential for richer data integration.

Findings

Multimodal LLMs can improve diagnostic accuracy in mental health.

They enable real-time monitoring and personalized interventions.

Integration of speech, images, and physiological signals enhances understanding of mental states.

Abstract

Generative Large Language Models (LLMs) are transforming mental health care by enabling the generation and understanding of human-like text with increasing nuance and contextual awareness. However, mental health is a complex, multidimensional domain that often requires richer sources of information beyond text. This narrative review explores the emerging role of Multimodal LLMs (MLLMs), which are models that integrate diverse input modalities such as speech, images, video, and physiological signals, to incorporate the multifaceted nature of mental states and human interactions. We first outline the foundational principles of MLLMs and their distinction from traditional text-only LLMs. We then synthesize recent empirical studies and experimental applications of MLLMs in mental health research and clinical settings, highlighting their potential to improve diagnostic accuracy, enable real-time monitoring, and support context-aware, personalized interventions. Finally, we outline opportunities for future research and innovation, and discuss key implementation challenges in MLLM-based mental health care.

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