Mental health
They found that even frontier models had a word error rate of 35% in Kannada clinical interviews, while only 64% of personal identifiers in clinical notes were correctly detected and removed. Because the first point of contact is usually not a psychiatrist trained to screen for and treat the condition, dr Lekhansh Shukla, assistant professor, Centre for Addiction Medicine, Nimhans, said depression is often missed.
Nimhans director Dr Y C Janardhan Reddy said early identification of depression could also strengthen research and give greater confidence in diagnosis if differences are accounted for using AI models. Bengaluru: Depression may not always announce itself as depression. It can show up as fatigue, a troubled stomach or simply a feeling that something is amiss. Now, an AI model being developed by researchers could help doctors detect those less obvious signs by analysing doctor-patient conversations — including the words, emotions and subtle cues that may otherwise go unnoticed. Researchers first conducted a pilot study to benchmark existing AI systems.
The two-year project, running from Sept 2026 to Aug 2028, will develop and evaluate AI-assisted approaches in five languages — Kannada, Hindi, Bengali, Assamese and English. About 4,500 participants, including 4,000 patients and 500 volunteers from Nimhans and LGBRIMH, will be enrolled. It will then translate conversations into English while retaining psychiatric terminology, idioms of distress and mixed-language speech — nuances that can often be lost during translation.
The model will listen to conversations between physicians and patients and convert them into text. The system will remain a clinician-facing tool and will not be made directly available to the public, partly to prevent overdiagnosis.