We acknowledge and thank Michael Howell, MD MPH, Bakul Patel, MSEE MBA, Matthew Thompson, DPhil MPH, Joseph Dooley, MPA, and David Steiner, MD PhD for reviewing and providing helpful feedback on this manuscript. LLMs MENTAL HEALTH 2 Abstract Global rates of mental health concerns are rising and there is increasing realization that existing models of mental healthcare will not adequately expand to meet the demand. With the emergence of large language models (LLMs) has come great optimism regarding their promise to create novel, large-scale solutions to support mental health. Despite their nascence, LLMs have already been applied to mental health-related tasks. In this review, we summarize the extant literature on efforts to use LLMs to provide mental health education, assessment, and intervention and highlight key opportunities for positive impact in each area. We then highlight risks associated with LLMs' application to mental health and encourage adoption of strategies to mitigate these risks. The urgent need for mental health support must be balanced with responsible development, testing, and deployment of mental health LLMs. Especially critical is ensuring that mental health LLMs are fine-tuned for mental health, enhance mental health equity, adhere to ethical standards, and that people, including those with lived experience with mental health concerns, are involved in all stages from development through deployment. Prioritizing these efforts will minimize potential harms to mental health and maximize the likelihood that LLMs will positively impact mental health globally. To overcome inadequate access to effective and equitable mental healthcare, large-scale solutions are needed.

What is the true heart of mental healthcare -- innovation or humanity? Can virtual therapy ever replicate the profound human bonds where healing arises? As artificial intelligence and immersive technologies promise expanded access, safeguards must ensure technologies remain supplementary tools guided by providers' wisdom. Implementation requires nuance balancing efficiency and empathy. If conscious of ethical risks, perhaps AI could restore humanity by automating tasks, giving providers more time to listen. Yet no algorithm can replicate the seat of dignity within. We must ask ourselves: What future has people at its core? One where AI thoughtfully plays a collaborative role? Or where pursuit of progress leaves vulnerability behind? This commentary argues for a balanced approach thoughtfully integrating technology while retaining care's irreplaceable human essence, at the heart of this profoundly human profession. Ultimately, by nurturing innovation and humanity together, perhaps we reach new heights of empathy previously unimaginable.

Providing mental healthcare to individuals with limited English proficiency (LEP) remains a pressing problem within psychiatry. Because the majority of individuals trained in providing psychiatric care are English speakers, the quality of mental healthcare given to LEP patients is significantly lower than that provided for English speakers. The provision of mental healthcare is contingent on communication and understanding between the patient and healthcare provider, much more so than in the realm of physical healthcare, and English speakers are often unable to comprehend figurative language such as metaphors used by LEPs. Hence, Figurative Language Translation is invaluable to providing equitable psychiatric care. Now, metaphor has been shown to be paramount in both identifying individuals struggling with mental problems and helping those individuals understand and communicate their experiences. Therefore, this paper aims to survey the potential of Machine Translation for providing equitable psychiatric healthcare and highlights the need for further research on the transferability of existing machine and metaphor translation research in the domain of psychiatry.

Artificial Intelligence (AI) for supporting healthcare services has never been more necessitated than by the recent global pandemic. Here, we review the state-of-the-art in AI-enabled Chatbots in healthcare proposed during the last 10 years (2013-2023). The focus on AI-enabled technology is because of its potential for enhancing the quality of human-machine interaction via Chatbots, reducing dependence on human-human interaction and saving man-hours. Our review indicates that there are a handful of (commercial) Chatbots that are being used for patient support, while there are others (non-commercial) that are in the clinical trial phases. However, there is a lack of trust on this technology regarding patient safety and data protection, as well as a lack of wider awareness on its benefits among the healthcare workers and professionals. Also, patients have expressed dissatisfaction with Natural Language Processing (NLP) skills of the Chatbots in comparison to humans. Notwithstanding the recent introduction of ChatGPT that has raised the bar for the NLP technology, this Chatbot cannot be trusted with patient safety and medical ethics without thorough and rigorous checks to serve in the `narrow' domain of assistive healthcare. Our review suggests that to enable deployment and integration of AI-enabled Chatbots in public health services, the need of the hour is: to build technology that is simple and safe to use; to build confidence on the technology among: (a) the medical community by focussed training and development; (b) the patients and wider community through outreach.

Have you ever heard of emotion artificial intelligence (AI)? Emotion AI, or affective AI, is a field of computer science that helps machines gain an understanding of human emotions. The MIT Media Lab and Dr. Rosalind Picard are the premier innovators in this space. Through their work, they sparked the idea to help machines develop empathy. Empathy is a complex concept with a lot of strings attached to it, but on a basic level, it means having an understanding of another person's emotional states.

You will be part of the Machine Learning (ML) team and contribute to building robust, production-ready models. You will leverage our extensive speech dataset while experimenting with a multitude of deep-learning architectures to explore state-of-the-art speech analysis methods to solve a variety of classification and regression tasks. Working alongside our cloud engineering team, you will help deploy these models and ensure they stay performant in a wide range of customer-facing applications. Minimum Qualifications • M.S./Ph.D. in Computer Science or equivalent or B.S. with 5 years of experience in building production-grade machine learning models in industry and/or academic research settings • Strong programming skills in python with extensive experience with the scientific and deep-learning stack (numpy, pandas, numba, torch, tensorflow, jupyter) • Background in speech processing or audio classification • A proven track record of building end-to-end neural network models and presenting results to colleagues • Experience optimizing the compute performance of models for production • Ambitious team player with strong communication skills (oral and written) • Experience implementing and experimenting with cutting-edge ML techniques from the literature Kintsugi is on a mission to scale access to mental healthcare for all. We are developing novel voice biomarker software to detect signs of depression and anxiety from short clips of free form speech. Awarded multiple distinctions for AI technology and recently named one of Forbes' Top 50 AI companies to watch in 2022, Kintsugi helps to close mental health care gaps across risk-bearing health systems, ultimately saving time and lives.

The pandemic, the recess of economics, and the war in Europe are all factors contributing to feelings of negativity and depression. However, access to quality mental healthcare varies from country to country. In some areas, it might be hard to find a qualified professional, or the supply might be lower than the demand. All these have contributed to the rapid proliferation of mental health provider apps. Other technological advances, such as AI chatbots may play a critical role in mental healthcare in the coming future. Let's take a look at both the benefits and limitations of this technology.

The COVID-19 pandemic has had a major impact on mental health across the globe. Depression is predicted to be the leading cause of lost life years due to illness by 2030. At the same time, less than 1 in 5 people receive appropriate treatment. Digital interventions – which package up psychotherapeutic components into a computer program or mobile app – have been proposed as a way of meeting the unmet demand for psychological treatment. As digital interventions are becoming increasingly adopted within both private and public healthcare systems, researchers asked if digital interventions are as effective as traditional face-to-face therapy, whether the benefits are also found in public healthcare settings and what is the role of human support.

The welfare of mental health has become as pertinent as physical healthcare nowadays. If artificial intelligence is used for the diagnosis of mental health, the results would be more accurate and it would be much easier for psychologists to find solutions. It would help a lot in the preliminary check-up and initial treatment. Analytics Insight has selected five such startups that are using AI technology in mental health treatment. Sentio Solutions develops biomarkers and digital therapeutics using artificial intelligence to come up with innovative ways of treating mental health issues.

According to the report, suicide is among the top 20 leading causes of death worldwide. Over the years, Artificial Intelligence (AI) tools have been used to fill gaps in mental health care: be it the diagnosis or detection of the early signs of mental health issues. Now, researchers at the University of South Carolina's Viterbi School of Engineering (USC's VSE) have developed an algorithm that can identify individuals in real-life social groups who can be trained as gatekeepers to spot suicidal tendencies. "Gatekeeper training" is an intervention training method approved by WHO. A suicide prevention gatekeeper can be any community member.