In a groundbreaking advancement for mental health technology, Nigerian-born professor Nchebe-Jah Iloanusi, based at the College of Staten Island in New York, has developed an Artificial Intelligence (AI) system capable of predicting the onset of depression in real time—before clinical symptoms become visible2.
The AI model uses a multimodal approach, analyzing signals from heart rate variability (HRV), sleep patterns, and speech biomarkers extracted using large language models (LLMs). By fusing these physiological and behavioral indicators, the system can detect subtle shifts in emotional and cognitive states that often precede depression by days or even weeks3.
Experts have hailed the innovation as a potential game-changer in early mental health intervention, especially in a world where depression remains one of the leading causes of disability and suicide. Traditional diagnostic methods often rely on self-reporting and periodic clinical evaluations, which can delay timely treatment. Iloanusi’s model offers a proactive alternative that could revolutionize how mental health is monitored and managed globally.
The research has been published in peer-reviewed journals and is already attracting interest from healthcare providers, tech companies, and mental health organizations seeking to integrate predictive AI into their services.