If you think about the last time you, or someone close to you, felt overwhelmed, uncertain, or quietly struggling to make sense of what was happening within, there is a good chance that, alongside speaking to someone or sitting with those feelings, there was also a new, instinctive pull toward a screen. Perhaps you could open something like ChatGPT, or another conversational tool and type out a thought; waiting for a response which arrives immediately. Just like that - in that small, unremarkable moment - something about the way we seek care has already begun to shift.
What makes this particularly interesting is that it doesn’t necessarily feel like a shift at all. Artificial intelligence hasn’t entered mental healthcare as a visible replacement for human care, but as a quiet extension of it; something which sits alongside our existing habits. Systems which respond to emotional distress, applications which track mood and behaviour over time, or platforms that suggest ways of coping when we are not quite sure how to proceed: gently reshaping them.
Much of this arrives wrapped in a language of promise, and not without reason. In many parts of the world, where mental health systems remain stretched or inaccessible, even a limited form of support can make a meaningful difference. It would be difficult to deny that, for some individuals, these systems offer a kind of presence in moments where there might otherwise be none. But if we remain with this development for a moment longer, and allow ourselves to look just a little beneath the surface, a different kind of question begins to emerge: not about whether these systems are useful, but about how their presence is reshaping the conditions under which care takes place.
Mental healthcare, if we think of it in traditional, human terms, has never really only about solving problems or reducing symptoms: it has always been about the experience of being with another person. When artificial systems begin to occupy parts of this space, however, something subtle but begins to shift. It is certainly true that many people feel heard when they interact with LLMs, and that feeling matters, because in moments of vulnerability, the simple act of being acknowledged can bring relief. More significantly, the responses generated are often now coherent, emotionally attuned and reassuring in ways which were previously impossible. And yet there is a distinction here that we do not always pause to consider.
Relationships between humans are rarely smooth. They involve pauses; misinterpretations; moments of discomfort, and the need to repair what has gone wrong. It is often within these imperfect spaces that something deeper begins to form. Artificial systems are designed to remove that friction, offering responses which are immediate, carefully composed and consistently aligned with what we expect to hear. Over time, and often without us noticing, repeated exposure to such interactions may begin to shape how we imagine care itself, what we expect from others, and even how the degree of difficulty we are willing to tolerate within our human relationships.
Alongside this relational shift, another change quietly unfolds, this time around responsibility. One of the defining features of mental healthcare has always been that someone is accountable: whether a clinician, an institution or a system of care. With artificial intelligence becoming more involved in guiding or shaping those decisions however, that clarity can begin to blur, as responsibility is distributed across systems, developers, and institutions. It is increasingly difficult to say with absolute certainty who, exactly, is responsible for what. Iin that diffusion, the moral structure of care begins to loosen.
Underlying all of this is the question of inequality. While AI is frequently presented as a way of expanding access, it is also shaped by the same structures of power which existed long before it: from the data it is trained on to the infrastructures which supports it, and this raises the possibility that systems designed to increase inclusivity may also, under certain conditions, reproduce more nebulous processes of exclusion. When we begin to pull these disparate threads together, it becomes clear that AI is not simply adding something new to mental healthcare; it is participating in a gradual transformation of how care is experienced, how responsibility is understood, and how distress itself is interpreted.
It is within this broader landscape that my forthcoming work, Future-Proofing the Mind, takes shape, not as a reaction to technology in isolation, but as an attempt to understand how these changes unfold across different levels of human experience. The first volume explores the shifting nature of relationships, development, and therapeutic encounter in the presence of artificial systems, and the second extends the discussion into questions of power, governance, and structural inequality. The concern which runs through this work is not that AI will inevitably undermine mental healthcare, but that, without careful reflection, certain elements of care - those grounded in responsibility, vulnerability, and ethical presence - may gradually recede, not by sudden replacement, but through a quieter process in which efficiency is seen as a sufficient stand-in for adequacy, and responsiveness begins to take the place of complex, nuanced relationships.
At the same time, this is not a moment which calls for rejection or resistance in any simplistic sense, because the trajectory of AI is not fixed, and its role in mental healthcare will continue to be shaped by the choices we make, whether in design, policy, or everyday practice. The important question is not simply how advanced these systems become, but how thoughtfully they are integrated into forms of care which continue to value dignity, agency, and meaningful human connection. This may involve preserving spaces where human presence remains central (particularly in contexts that require accountability and ethical engagement) or designing systems which support, rather than replace, judgment. It may mean ensuring that responsibility remains visible rather than diffuse or addressing inequality as a foundational concern rather than an afterthought. It may also require us to rethink what we mean by progress; recognising that in mental healthcare, what appears slow or imperfect is often inseparable from what makes care meaningful, and that not everything which can be optimized for maximal efficiency should be.
As we mark Mental Health Awareness Week 2026 this reflection becomes especially important, not only as a moment to acknowledge distress and advocate for access, but as an opportunity to consider how the very idea of care is being reshaped in ways which are gradual, subtle, and yet deeply consequential. In the end, the question is not whether AI will become more capable (it almost certainly will) but whether, in the process, we will remain capable of recognising what cannot be replaced, what must be held with care, and what it means, even now, to be present for another human being in a way that no system, however advanced, can fully inherit.
References:
Beg M. J. (2025). Responsible AI Integration in Mental Health Research: Issues, Guidelines, and Best Practices. Indian journal of psychological medicine, 47(1), 5–8. https://doi.org/10.1177/02537176241302898
(Dr. Mirza Jahanzeb Beg is a psychologist and author and heads the Center for Advanced Behavioral Policy Innovation and Leadership (CABPIL), KI, Coimbatore. He is a professor of psychology with research interests in behavioural science, AI, technology, public policy, geopolitics, and philosophy. The views expressed are personal. He can be reached at mirzajahanzebbeg@gmail.com).