The application of machine learning (ML) in detecting, diagnosing, and treating mental health disorders is garnering increasi ng attention. Traditionally, research has focused on single modalities, such as text from clinical notes, audio from speech samples, or video of interaction patterns. Recently, multimodal ML, which combines information from multiple modalities, has demonstrated significant promise in offering novel insights into human behavior patterns and recognizing mental health symptoms and risk factors. Despite its potential, multimodal ML in mental health remains an emerging field, facing several complex challenges before practical applications can be effectively developed. This survey provides a comprehensive overview of the data availability a nd current state-of-the-art multimodal ML applications for mental health. It discusses key challenges that must be addressed to advance the field.

Early diagnosis of mental disorders and intervention can facilitate the prevention of severe injuries and the improvement of treatment results. Using social media and pre-trained language models, this study explores how user-generated data can be used to predict mental disorder symptoms. Our study compares four different BERT models of Hugging Face with standard machine learning techniques used in automatic depression diagnosis in recent literature. The results show that new models outperform the previous approach with an accuracy rate of up to 97%. Analyzing the results while complementing past findings, we find that even tiny amounts of data (Like users' bio descriptions) have the potential to predict mental disorders. We conclude that social media data is an excellent source of mental health screening, and pre-trained models can effectively automate this critical task.

Through our work in the Microsoft AI for Accessibility program, we have learned there are big gaps in mental health services around the globe. In some countries, there may only be one mental health professional per 100,000 people. When paired with the reality that 1 in 5 people have a mental health condition, we are asking how technology can and should be involved. In February, we shared our call for project proposals that aim to accelerate mental health research, data insights, and innovations using AI, and today we want to highlight the projects we're supporting. Of the 89% of people who screened positive for major depression through Mental Health America's online survey last year, 79% do not want to pursue psychotherapy or medications, yet 50% want access to digital tools.