With technological advancements and digital transformation, the world has been achieving highs for the past few years. But everything comes at a price. Most people nowadays are facing some form of mental disorders leading to suicides. As per the report by the World Health Organisation (WHO), nearly 8 lakh people die due to suicide each year worldwide, which indicates one death every 40 seconds. Suicide is a global phenomenon, and 79% of suicides occurred in low and middle-income countries. And the suicide rate has been increasing day by day. To address this issue, digital technologies like artificial intelligence (AI) can offer great help. With AI and its tools, the number of suicide cases can notably be lowered.

Artificial intelligence and machine learning (ML) tools are being adopted to minimise cases and enable faster responses. AI algorithms detect behaviour and mental activities of people based on trained data sets and assist in suicide management. Addressing the suicide problem comes down to identifying patterns and acting quickly to avoid negative implications. On the other hand, AI tools assist counsellors and responders to interact with suicide victims and advocate the problem early. Suicide patterns need detailed analysis, and AI tools connect trends accurately. AI recognises patient behaviours likely to lead to suicide that makes it a perfect tool for this role. ML algorithms analyse patterns and offer recommendations based on the mental health conditions of patients.

Although AI has been used in healthcare since the 1990s to improve disease detection and various indices of wellness, its applications to address mental health-related issues or prevent suicides were never wide. However, AI has gradually enhanced the speed and accuracy of diagnosis and applied decision trees to guide treatment selection with mental health.

A new approach to therapy involves conversational bots or chatbots designed to accelerate human-like conversation using voice or text responses. Chatbots, computer programs, can convey mental interventions for depression and tension dependent on cognitive behavioural therapy (CBT). As chatbots uniquely respond to presented dialogue, they can tailor interventions to a patient’s emotional state and clinical requirements.

AI has also been incorporated into suicide management to improve patient care in other areas. AI assessment tools have been shown to anticipate short-term suicide risk and make treatment recommendations that are as good as clinicians.

Current evaluation and management of suicide risk are still highly subjective. To improve results, more objective AI strategies are required. Promising applications include suicide risk prediction and clinical management. As suicide is influenced by a variety of psychosocial, biological, environmental, economic and cultural factors, AI can be used to explore the collaboration between these factors and suicide outcomes.

A growing number of researchers and technology companies are using AI to monitor suicide risk through online activity. This builds on emerging evidence that language patterns on social media and methods of smartphone use can indicate psychiatric issues. In most suicide cases, natural language processing (NLP) is used to analyse the online activity of users on social media platforms for suicidal behaviours. This may be integrated with ML techniques to compare and contrast findings across and within platforms to determine patterns of behaviour and how this may relate to risk severity.

For example, Tech giant Facebook is contributing to preventing suicides by working with counsellors whenever suicide content emerges. Facebook added a feature for reporting suicide information after which the company reviews the information for reporting. Emergency responders dealing with suicide work with Facebook by responding as early as possible. AI at Facebook uses algorithms to detect cases. Socialisation patterns speak about individual characters and Facebook’s algorithms match suicide-related content and inform the relevant authorities. The Facebook AI tools scan through posts and the same applies to videos within a short time. Facebook has engaged with over 100 emergency responders because of its AI tools for suicide detection. These tools from Facebook work similarly to flag posts linked with drug markets and terror organisations.

On the other hand, another tech giant Google’s AI algorithms search information from the web and diffuse through user content to understand suicide cases. Suicide victims use the web such as YouTube to search for suicide videos, and Google deployed ML tools to detect such cases. These tools flag such accounts and bring this to the attention of Google. The ML algorithms at Google work by connecting social patterns and predict users with high chances of committing suicide. Google partners with healthcare institutions, psychologists and counsellors to report suicide with much success.

AI and ML applications hold unique promise to enable precision medicine in the prevention of suicide, particularly given their ability to handle large and complex datasets. These approaches may crucially inform the early detection of suicide risk, triage, and treatment development, with important methodological and statistical cautions. The application of natural language processing to social media in particular, and integration of AI with real-time suicide risk assessments, holds unique promise to impact the prevention of suicide on a broad scale.

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