Harnessing AI for Suicide Prevention Through Real-Time Monitoring

Suicide remains one of the most complex and heartbreaking public health challenges worldwide. A significant hurdle in prevention efforts is accurately identifying when someone is struggling, as suicidal thoughts and behaviors can fluctuate rapidly and may not always be evident during clinical interactions.

In our increasingly digital world, people routinely use devices to monitor various aspects of their health, from physical activity to sleep patterns. Researchers are now exploring how similar technology can be leveraged to better understand mental health states. One such method is Ecological Momentary Assessment (EMA), which collects real-time data via smartphones or wearable devices, providing continuous insights into a person's mood, thoughts, behaviors, and environmental context. EMA can operate actively, prompting individuals to report their feelings and thoughts, or passively, automatically gathering data through sensors.

Studies have demonstrated that EMA is a safe and effective tool for monitoring suicide risk, capturing a nuanced picture of an individual’s mental state without increasing risk. This granular information opens doors to creating personalized interventions, known as adaptive interventions, that respond in real-time. For instance, a device might detect signs of distress and prompt the user to follow safety plans—strategies developed in collaboration with mental health professionals—to help manage crises.

While promising, these approaches raise important questions: What specific data changes should trigger alerts? When is the optimal moment to intervene? And what form should support take?

Artificial intelligence (AI) and machine learning are crucial in addressing these questions. By analyzing subtle patterns in behavioral and emotional data, AI models can predict suicide risk more accurately than traditional tools. These models are already being used to forecast individual risk levels and check suicide rates across populations, offering valuable insights. However, concerns about privacy, data diversity, and fairness remain, highlighting the need for careful implementation.

Trust in AI systems is paramount for successful integration into mental health care. Explainable AI, which provides transparency into how decisions are made, can help clinicians understand and rely on these tools. Ultimately, combining AI with real-time monitoring offers new hope in the fight against suicide. While not a cure-all, these advances enable more timely, personalized support, potentially saving lives.

Source: https://medicalxpress.com/news/2025-05-ai-suicide-real-big-mental.html