Mental health is more than the absence of mental disorders; it is a state of well-being that enables people to cope with life’s stresses, realise their abilities, and participate meaningfully in society. Yet in India, mental health remains a largely neglected dimension of healthcare. Approximately 15% of adults experience mental health issues requiring professional intervention, but awareness, access, and treatment are severely limited. Government data shows that "70% to 92% of people with mental disorders do not receive proper treatment due to lack of awareness, stigma, and shortage of professionals.”
The infrastructure gap is stark. With roughly 0.75 psychiatrists per 100,000 people, below the WHO-recommended minimum of 1 per 100,000, and most specialists concentrated in urban areas, rural populations continue to be underserved. Many medical officers at primary health centres lack confidence to diagnose and treat common mental disorders, even after short training programmes, and general practitioners often miss cases, widening the treatment gap further.
Considering this, it becomes important to understand how AI-driven solutions are looking to address these gaps, expand access, and make mental health care more accessible and destigmatised across the country.
Mental health scenario in India
Mental health challenges in India remain a significant public health concern. According to government data, “About 10.6% of Indian adults – roughly 11 out of every 100 adults – were living with a diagnosable mental health disorder,” as reported by the 2015-16 National Mental Health Survey (NMHS) conducted by the National Institute of Mental Health and Neurosciences (NIMHANS).
The survey further revealed that approximately 15% of India’s adult population experiences mental health issues requiring intervention. The lifetime prevalence of mental disorders was 13.7%, indicating that around 14 out of every 100 people in India have experienced a mental disorder at some point in their lives. Mental health disorders are more prevalent in urban areas (13.5%) compared to rural areas (6.9%).
Gender differences are also notable. According to a 2019 NIMHANS study, mental health disorders are more prevalent among women (20%) than men (10%). Women in India are particularly susceptible to conditions such as depression, anxiety, and somatic complaints compared to their male counterparts.
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Suicide rates in India continue to rise, highlighting the urgent need for mental health interventions. The 2023 National Crime Records Bureau (NCRB) report, Accidental Deaths & Suicides in India, recorded over 1.71 lakh suicides in the country. The report also revealed a significant gender disparity: males accounted for 72.8% of all suicides, while females accounted for 27.2%.
Access to mental health care remains a major challenge. “The 2015-16 NIMHANS survey also highlighted that 70% to 92% of people with mental disorders do not receive proper treatment due to the lack of awareness, social stigma, and a shortage of professionals.” The shortage of mental healthcare professionals exacerbates the issue. While the World Health Organisation (WHO) recommends at least 3 psychiatrists per 100,000 people, a 2019 study by Garg et al., published in the Indian Journal of Psychiatry, found that India has only 0.75 psychiatrists per 100,000 people.
Can AI bridge the gap?
Dr Jayashree Dasgupta, Co-founder of SocioScript, highlighted AI’s growing potential in mental health screening, while cautioning that it cannot replace trained professionals. “AI has significant promise, especially when it comes to screening. In terms of actual diagnosis, we still have a long way to go,” she said. “But screening is where AI can make a real difference.”
Dr Dasgupta described a tiered, pyramid-like approach: “AI can be used to screen large populations first. Those who meet certain thresholds or are flagged as high-risk can then be referred to mental health professionals, whether psychiatrists, psychologists, psychiatric social workers, or trained personnel using a task-shifting model. In this way, AI helps bridge the gap without replacing human expertise.”
“AI cannot substitute for mental health professionals. But it has a critical role in widening access to screening, ensuring more people get identified early and connected to the care they need,” she added.
Challenges to responsible AI in mental health
Dr Dasgupta cautioned that AI in mental health carries significant risks if not implemented responsibly. She noted that more people are turning to ChatGPT and other AI tools for mental health concerns, but emphasised the need for structured support alongside these platforms.
“Whenever a concern is being flagged, access to national helplines or other support must be provided,” she said. “The person also needs encouragement to seek professional help, because AI cannot replace the clinician in this case.”
Reflecting on past internet risks, she drew parallels between search engines and AI tools. “Several years ago, if you typed in Google, ‘how do I commit suicide,’ you would get all the information. When people at the Public Health Foundation of India worked on this issue, they ensured that the first search results provided helpline numbers and professional support, not methods. Similarly, AI chatbots should be designed to guide people responsibly.”
She added that some AI platforms are already taking steps in this direction. “For example, ChatGPT provides helpline numbers if a user reports feeling depressed or suicidal. I am not sure how accessible those numbers are, but this is something that could be implemented at the design stage itself. That is one way AI could support mental health responsibly.”
Dr Dasgupta also highlighted the risk of reinforcing stigma. She emphasised that AI is not a standalone solution for mental health challenges. “It can facilitate or aid in certain ways, but we still need traditional methods, creating awareness first and working towards destigmatisation. Only with increased awareness about both mental health and the limitations of AI can we change the landscape.”
Adding to this, Ms Sujaya Krishnan, former Joint Secretary, Ministry of Health and Family Welfare, GOI, noted that India’s mental health ecosystem has evolved significantly. What was once heavily stigmatised is now openly discussed, and there is growing interest in well-being, especially after the COVID-19 pandemic.
“AI can aid in initial screening because it can analyse speech, text, and behavioural patterns, helping identify mental health issues earlier. But this is in no way a replacement for human professionals; it is to aid them,” Krishnan said.
She explained that India faces structural challenges, including shortages of trained personnel, inequities in rural areas, fragmented care pathways, and an increasing elderly population. “We have a shortage of manpower, inequities, fragmented care pathways, and no continuity. At the same time, we have a large youth population and an increasing elderly population. While India has a demographic dividend, it also comes with challenges.”
Highlighting AI’s potential role among younger populations, Krishnan pointed out that technology can sometimes lower psychological barriers to seeking help. “For many young people, AI tools allow them to engage in compassionate and non-judgmental conversations. They may feel more comfortable opening up than they would in a formal clinical setting. This makes AI useful, especially for initial screening and early engagement,” she said.
However, she also warned about the dangers of misuse if AI tools are not scientifically validated and ethically governed. Referring to global incidents, she said, “There have been cases where individuals shared suicidal thoughts with AI platforms and later attempted suicide. If a psychologist or psychiatrist had been involved, human judgment could have intervened. That is why strong validation is critical.”
“AI can augment healthcare delivery, but it cannot replace the human element, especially in complex and sensitive areas such as mental health,” she emphasised.
What is the future of AI in mental healthcare?
Dr Dasgupta emphasised that the future role of AI in mental healthcare must be understood within clear boundaries, particularly the distinction between screening and diagnosis. According to her, while AI shows promise in identifying early risk signals, it is far from being capable of replacing clinical judgment.
“Screening and diagnosis are very different,” she said. “We are definitely not at a stage where AI can diagnose mental health conditions. Mental health is very different from conditions that can be diagnosed through imaging or radiological data. For instance, in autism, a trained clinician might pick up subtle behavioural cues when a child simply walks into a waiting room. That level of nuance and contextual understanding cannot be replicated by AI.”
Dr Dasgupta also highlighted AI’s potential as a capacity-building tool for the healthcare system. Rather than focusing only on producing more specialists, she suggested using technology to strengthen the skills of the existing workforce.
“AI can play a major role in training and awareness-building among healthcare professionals,” she noted. “We do not necessarily need to create only more psychiatrists. Instead, we can equip doctors, nurses, and frontline workers with basic mental health training so they can identify red flags early and support timely referrals.”
On the question of responsible adoption, Dr Dasgupta stressed that no single stakeholder can shoulder the responsibility alone. She called for coordinated efforts across policy, healthcare, and technology sectors. “Responsible AI use is a complex issue,” she said. “You cannot place the entire burden on legal frameworks, and you also cannot leave it only to individuals or healthcare professionals. Collaboration across policymakers, technologists, and health systems is essential if we want solutions that genuinely benefit society, especially in mental health.”
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Adding a policy and systems perspective, Ms Krishnan said that AI’s future impact lies in its integration across multiple layers of care rather than isolated hospital-based applications.
“We have to integrate AI into primary healthcare, school health programmes, elderly care services, and community-led initiatives,” she said. “By combining health records, diagnostic data, personal and environmental factors, and even genetic profiles, AI can support predictive analysis.”
According to Krishnan, such integrated data systems can help health professionals better understand risk patterns and disease progression. “This approach helps in assessing risk factors, prognosis, and the likelihood of mental health conditions, especially mild to moderate disorders such as depression, anxiety, etc.,” she added.
She acknowledged that India’s shortage of trained mental health professionals makes technology-assisted care increasingly relevant. However, she cautioned against unregulated use. “We do not have enough personnel, and training takes time,” Krishnan said. “For primary care and initial screening, AI can support scale and outreach, but strong guardrails and safeguards must always be in place because the data is sensitive.”
Krishnan also emphasised the importance of privacy, ethics, and system readiness in mental health applications. “Mental health data is extremely sensitive. We need stronger validation processes, robust datasets, and clear ethical safeguards. At the same time, scalability, accessibility, affordability, sustainability, and continuity of care are critical. Mental healthcare cannot remain limited to secondary or tertiary hospitals; it must reach community health settings.”
Looking ahead, she reiterated that AI’s role will remain supportive rather than substitutive. “AI will never fully replace a human clinician, especially in complex or subjective cases. But it can help with screening, assessment, early intervention, and predictive insights. If implemented responsibly, AI has enormous potential to make mental healthcare more accessible and effective in India,” she said.
This story is done in collaboration with First Check, which is the health journalism vertical of DataLEADS.
