Pune: A recent study conducted by the Maharashtra University of Health Sciences (MUHS) on 130 postgraduate medical students has underscored a critical shift in healthcare thinking: emotions are not peripheral to health; they are foundational.For decades, medical studies have recognised that emotional state influences stress regulation, behaviour, and long-term health outcomes, with chronic emotional dysregulation associated with increased allostatic load, impaired immunity, and a higher risk of non-communicable diseases. Yet, despite this understanding, emotional assessment in clinical and institutional settings remained largely dependent on self-reporting — an approach that is often unreliable and influenced by awareness, recall, interpretation, and an individual’s ability or willingness to articulate inner states.Published in the peer-reviewed Journal of Rare Cardiovascular Diseases, the MUHS study examines emotional assessment through an objective, AI-driven lens. The research brings emotional measurement closer to the level of seriousness traditionally reserved for physiological indicators, positioning emotional state as a measurable and clinically relevant input rather than a subjective afterthought.The research compared established psychological tools — such as the Beck Anxiety Inventory (BAI) and the Perceived Stress Scale (PSS) — with Emoscape, an AI-based emotional intelligence platform developed by Nihilent.Unlike traditional surveys, Emoscape does not rely on language or questionnaires. Instead, it uses a standard web camera to analyse subtle, involuntary human micro-movements. Using advanced machine learning, the platform interprets these signals through the lens of the nine core emotions (Navarasas) from the ancient Natyashastra tradition. The process is entirely non-invasive, requiring no sensors or wearables.A total of 130 postgraduate students participated, generating over 780 individual test records. To process this complex data, researchers utilised “K-means clustering”, a popular unsupervised machine learning algorithm, to identify meaningful emotional patterns.The study found that the emotional patterns identified by AI closely corroborated the outcomes of conventional psychological tools. This validation is a major milestone, proving that AI can accurately surface emotional states that were previously only detectable through lengthy manual evaluations.The practical value of this technology was highlighted by an anonymised case at MUHS. A postgraduate student was assessed as only “mildly vulnerable” by standard anxiety scales and a psychiatric interview. However, an Emoscape assessment conducted in the same setting revealed a significantly higher level of internal emotional distress. This discrepancy prompted faculty to provide closer attention and follow-up care, demonstrating how objective AI signals can catch red flags that human observation might miss.LC Singh, Founder and executive chairman of Nihilent, noted the significance of measuring the “invisible layer” of health. “For decades, healthcare measured the body with precision, while the emotional forces influencing behavior and recovery remained unmeasured,” Singh said. “Emoscape brings objectivity to this layer, enabling patterns to be understood in a scalable way. This will lead to more complete approaches to patient care.“While the MUHS study does not claim direct physiological causality, it reinforces the fact that emotional states exert a sustained influence on health. By moving beyond a total reliance on subjective reporting, the research points toward a future where emotional data is treated as a vital health indicator.The study concluded that emotions are no longer abstract concepts; they are measurable, actionable, and integral to the future of human health.
