The chatbot will see you now: how AI is being trained to spot mental health issues in any language

"Calls to the clinic helpline are being used to train an AI algorithm that researchers hope will eventually power a chatbot offering therapy in local African languages. One person in 10 in Africa struggles with mental health issues, but the continent has a severe shortage of mental health workers, and stigma is a huge barrier to care in many places. AI could help solve those problems wherever resources are scarce, experts believe."

"Someone probably won't say suicidal' as a word, or they will not say depression' as a word, because some of these words don't even exist in our local languages, says Nakatumba-Nabende. After removing patient-identifying information from call recordings, Nakatumba-Nabende's team uses AI to comb through them and determine how people speaking in Swahili or Luganda or another of Uganda's dozens of languages might describe particular mental health disorders such as depression or psychosis."

"In time, recorded calls could be run through the AI model, which would establish that based on this conversation and the keywords, maybe there's a tendency for depression, there's a tendency for suicide [and so] can we escalate the call or call back the patient for follow up, Nakatumba-Nabende says. Current chatbots tend not to understand the context of how care is delivered"

Makerere University AI Lab, Butabika hospital and Mirembe hospital use de-identified helpline call recordings to train AI models to recognize local expressions of mental disorders. AI processing maps how speakers of Swahili, Luganda and other Ugandan languages describe symptoms of depression, psychosis and suicidal risk when standard clinical vocabulary is absent. The approach aims to identify red flags, escalate urgent cases and enable follow-up calls where needed. The initiative responds to a severe shortage of mental health workers across Africa and widespread stigma that limits access to care, and seeks to adapt chatbots to local linguistic and care-context realities.

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