Future of mental health to be driven by AI powered analytics: Harshal Sawant

Global and India’s growing mental health crisis demands a proactive, rather than reactive, approach to care. As the stigma around mental health diminishes, future technology such as artificial intelligence (AI) is stepping in to transform the landscape.

According to Harshal Kamalakar Sawant, practice head, healthcare software services, Tata Elxsi, by harnessing AI-powered analytics, healthcare providers and researchers are moving closer to early detection, personalized interventions, and sustained monitoring, enabling a responsive and preventive mental health ecosystem.

Noting that the future of mental healthcare lies in a model that combines empathy with intelligence, Sawant said that AI-powered analytics offer an unprecedented opportunity to shift from reactive treatment to proactive, predictive care. With timely insights, continuous monitoring, and personalized interventions, individuals receive support when they need it most. As these technologies evolve, data privacy and ethical governance will be key to building trust. However, AI is not replacing human care, it is improving it, making mental health support accessible, adaptive, and attuned to individual needs, he added.

An impactful use of AI in mental health is its ability to detect early signs of psychological distress through multimodal data fusion and advanced pattern recognition. Maximising natural language processing (NLP), machine learning (ML), and temporal sequence modelling, AI systems can process and correlate diverse data streams. For example, transformer-based models can identify latent markers of depression or anxiety by analyzing changes in speech prosody, while recurrent neural networks (RNNs) and time-series models detect deviations in circadian rhythms or social engagement. These predictive models flag anomalies like reduced physical activity, disrupted sleep cycles, or withdrawal from communication often imperceptible to human observers. This enables clinicians to initiate timely, pre-emptive interventions and reduce the likelihood of symptom progression, he noted.

Mental health is dynamic. It is influenced by both internal states and external environments. AI-powered digital therapeutics like conversational agents and journaling applications utilize sentiment analysis, natural language understanding (NLU), and attention-based models to track affective markers, lexical patterns, and user engagement. This constant flow of data empowers caregivers and mental health professionals to see patterns like mood swings linked to specific life events or stressors that can inform more adaptive care strategies, he said.

Predictive analytics can assess an individual's unique behavioural data. AI models can identify that a person experiencing high social media activity late at night, reduced physical movement during the day, and negative sentiment in written posts is at heightened risk of depressive relapse. This triggers tailored recommendations such as a prompt to schedule therapy, engage in guided breathing exercises, or receive a check-in message from a mental health coach, he said.

In a clinical setting, AI can help assess patients by risk level, enabling mental health professionals to prioritize care for those most in need. In schools and workplaces, early-warning systems powered by AI can suggest individuals exhibiting signs of emotional fatigue, ultimately creating a more supportive and responsive environment, said Sawant.