Use of Artificial Intelligence-Based Strategies for Assessing Suicidal Behavior and Mental Illness: A Literature Review

Khan, Nighat Z; Javed, Muhammad Ali

doi:10.7759/cureus.27225

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…Prior reviews have focused predominantly on research undertaken in adjacent fields, such as applications of machine learning or chatbots to mental health more broadly [19][20][21][22]. Although several reviews have explored the use of machine learning [23][24][25][26] or AI-based strategies [27][28][29][30] in suicide prevention contexts, these have not focused on applications of LLMs specifically. Furthermore, although one commentary [31] and a review [24] focused on the integration of computational linguistics or natural language processing more broadly to suicide prevention, these reviews have not focused on LLMs specifically, nor have any explored applications of LLMs to self-harm.…”

Section: Discussionmentioning

confidence: 99%

Applications of Large Language Models in the Field of Suicide Prevention: A Scoping Review (Preprint)

Holmes,

Tang,

Gupta

et al. 2024

Preprint

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BACKGROUND Prevention of suicide is a global health priority. Around 800,000 individuals die by suicide yearly, and for every death, there are another 20 estimated suicide attempts. Large language models (LLMs) hold the potential to enhance scalable, accessible, and affordable digital services for suicide prevention and self-harm interventions. However, their use also raises clinical and ethical questions that require careful consideration. OBJECTIVE This scoping review aimed to identify emergent trends in applications of LLMs within the field of suicide and self-harm research. Additionally, it summarizes key clinical and ethical considerations relevant to this nascent area of research. METHODS Searches were conducted in four databases. Eligible studies described the application of LLMs for suicide or self-harm prevention, detection, or management. English-language peer-reviewed articles and conference proceedings were included, with no date restrictions. This review adhered to PRISMA-ScR standards. RESULTS Of the 533 studies identified, 36 met inclusion criteria, and an additional 7 more were identified through citation chaining, resulting in a total of 43 studies for review. A narrative synthesis approach was used to synthesize study characteristics, objectives, models, data sources, proposed clinical applications, and ethical considerations. Studies showed a bifurcation of publication fields with varying publication norms between computer science and mental health. While most studies (77%) focused on identifying suicide risk, newer applications leveraging generative functions (e.g., support, education, and training) are emerging. Social media was the most common source of LLM training data. BERT (Bidirectional Encoder Representation Transformer) was the predominant model used, although GPT (Generative Pre-trained Transformer) featured prominently in generative applications. Clinical applications of LLMs were reported in 60% of studies, often for suicide risk detection or as clinical assistance tools. Ethical considerations were reported in 33% of studies, with privacy, confidentiality, and consent strongly represented. CONCLUSIONS This evolving research area, bridging computer science and mental health, demands a multi-disciplinary approach. While open access models and datasets will likely shape this field, documenting their limitations and potential biases is crucial. High-quality training data is essential for refining these models and mitigating unwanted biases. Policies that address ethical concerns – particularly related to privacy and security when using social media data – are imperative. The emergence of generative AI signals a shift in approach, particularly in applications related to care, support, and education. Ongoing human oversight, whether through human-in-the-loop testing or expert external validation, is essential for responsible development and use.

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Section: Discussionmentioning

confidence: 99%

Applications of Large Language Models in the Field of Suicide Prevention: A Scoping Review (Preprint)

Holmes,

Tang,

Gupta

et al. 2024

Preprint

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“…AI-driven applications, such as those powered by AI, have the capability to provide users with reminders to engage in self-care activities, employ relaxation techniques, or adhere to prescription schedules (3). Furthermore, AI algorithms have the capability to identify initial indicators of mental health decline, so enabling prompt intervention and the prevention of exacerbating circumstances (4).…”

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confidence: 99%

Could Artificial Intelligence be a Therapeutic for Mental Issues?

Darzi

2023

Sci Insights

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The utilization of artificial intelligence (AI) possesses the capacity to fundamentally transform the landscape of mental health care through the provision of very valuable therapeutic interventions. AI possesses the capacity to evaluate extensive quantities of data, enabling it to aid in the identification of early signs of mental health concerns and provide tailored solutions that cater to specific requirements. The utilization of virtual therapists powered by AI has the potential to provide a secure environment for persons experiencing social anxiety or worries associated with stigma. This form of therapy offers continuous support and diminishes the obstacles that hinder individuals from seeking assistance. In addition, AI algorithms possess the capability to effectively identify and analyze patterns and trends derived from many sources, including social media posts and smartphone usage. This enables the assessment of an individual’s mental health and facilitates the provision of timely solutions. Through the utilization of natural language processing and sentiment analysis, AI has the potential to augment current therapeutic practices by proficiently monitoring patient advancements and analyzing therapeutic dialogues. Therefore, the incorporation of AI into the realm of mental health intervention holds promise in enhancing accessibility, cost, and efficacy, while mitigating the strain on conventional healthcare systems.

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Artificial Intelligence in Mental Health Care

Pandey,

Misra

2023

Artificial Intelligence and Machine Learning in Healthcare

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Use of Artificial Intelligence-Based Strategies for Assessing Suicidal Behavior and Mental Illness: A Literature Review

Cited by 4 publications

References 29 publications

Applications of Large Language Models in the Field of Suicide Prevention: A Scoping Review (Preprint)

Applications of Large Language Models in the Field of Suicide Prevention: A Scoping Review (Preprint)

Could Artificial Intelligence be a Therapeutic for Mental Issues?

Artificial Intelligence in Mental Health Care

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