Artificial General Intelligence Could Reshape Mental Health Care, Review Warns

Artificial general intelligence, the long-hypothesised class of systems capable of performing any intellectual task at or beyond human level, is projected by several research communities to arrive before 2030. That timeline, once confined to speculative essays and laboratory roadmaps, has now become the starting point for a rigorous examination of what such technology could mean for the human mind. A new systematic narrative review published in Discover Mental Health by Cristina Caja Moya and Elio Quiroga Rodríguez of the Universidad del Atlántico Medio in Las Palmas de Gran Canaria, Spain, argues that AGI represents a dual theoretical force for mental health: it could democratise psychological care on an unprecedented scale while simultaneously introducing existential, clinical and social risks that current health systems are not equipped to manage.

The review, conducted according to PRISMA guidelines and operationalised through an explicit PICOS framework, searched five databases, PubMed, Scopus, Web of Science, Dialnet and PsycINFO, for publications from 2013 to 2023. Two reviewers independently screened records at the title and abstract stages and again at full text, resolving disagreements through discussion until consensus was reached. Because AGI does not yet exist as a clinically deployable technology, the eligible literature is overwhelmingly theoretical, ethical or conceptual rather than experimental. The authors therefore adopted an explicit hierarchy-of-evidence framework that distinguishes empirical studies, secondary reviews, ethical and normative analyses, and theoretical or speculative work, weighting the synthesis accordingly. Of 72 records initially identified, 45 studies met the inclusion criteria and were synthesised thematically using NVivo software, with methodological quality appraised through author-developed criteria aligned with the SANRA scale for narrative reviews and the AACODS checklist for non-empirical sources.

The first major theme concerns diagnosis and treatment. AGI-adjacent systems, particularly large language models and multimodal architectures already deployed under the umbrella of narrow AI, have demonstrated genuine potential to support mental health assessment by integrating heterogeneous data streams: clinical notes, speech patterns, facial expression analysis, behavioural telemetry and self-reported symptoms. Conversational interfaces accessible around the clock and at negligible marginal cost could reduce stigma by offering an anonymous, judgement-free first point of contact for people reluctant to seek human help. For underserved regions where the ratio of mental health professionals to population is dangerously low, the prospect of near-universal access to competent psychological support is the review’s most compelling promised benefit.

Yet the same technical capabilities carry the mirror-image risks. The literature consistently warns, at the level of ethical and normative analysis, that automated emotional crisis management is fraught with danger: a system that handles a suicidal disclosure with a plausible but subtly wrong response could produce diagnostic error, false reassurance or lethal delay in escalation to human clinicians. The review stresses that such risks, documented for narrow AI in embryonic form, would be substantially amplified in any future AGI system, and that presentations involving psychosis and other severe mental illnesses pose particular challenges, because the conversational fluency of advanced models can mask a fundamental lack of clinical understanding and may even reinforce delusional content.

The second theme addresses governance. The review identifies emerging regulatory instruments, mandatory algorithmic audits, transnational data protection policies and clearer allocation of medico-legal accountability, as necessary preconditions for any safe integration of AGI into mental health care. Algorithmic audits would function as a technical safeguard, probing systems for bias, brittleness and unsafe behaviour before and during deployment, while data protection frameworks would govern the extraordinarily sensitive information that mental health applications inevitably collect. Accountability remains perhaps the thorniest question: when an autonomous-sounding system contributes to a harmful clinical outcome, the chain of responsibility spanning developers, deployers, clinicians and institutions must be legally defined in advance rather than litigated after tragedy.

The third and most philosophically provocative theme concerns human identity. Drawing on established work-identity and AI ethics literatures, the authors advance theoretical constructs such as ‘existential delaborisation’, the psychological dislocation that could follow if AGI displaces human beings from work that confers meaning, purpose and social standing. They also describe the algorithmic reconfiguration of biographical narratives: the possibility that AGI systems, by curating recommendations, memories and self-reflections, could subtly rewrite the stories people tell about themselves. These phenomena are presented explicitly as theoretical frameworks rather than empirically documented outcomes, but their implications intersect with well-established knowledge that unemployment, loss of occupational identity and social dislocation are robust predictors of depression, anxiety and substance misuse. Mass technological unemployment, should AGI fulfil the most aggressive projections, would therefore constitute not merely an economic problem but a population-scale mental health exposure.

The review also highlights the risk that biases embedded in training data could be perpetuated and amplified within therapeutic interventions. If the corpora on which AGI systems are trained under-represent certain cultures, languages, genders or diagnostic populations, the resulting systems may misinterpret idioms of distress, apply inappropriate diagnostic framings or deliver interventions calibrated to populations unlike the user. Because mental health care depends on trust, cultural sensitivity and nuanced understanding of individual context, such biases are not simply statistical inaccuracies but potential sources of harm and of widening inequity between well-served and marginalised communities.

What distinguishes this analysis from much of the public AGI debate is its disciplined separation of evidence from speculation. The empirical claims about diagnostic support and stigma reduction rest on studies of currently deployed narrow AI, while the warnings about existential and identity-level harms are clearly flagged as normative and theoretical extrapolation. This evidentiary transparency matters practically as well as intellectually: policymakers and clinicians reading the review can see precisely which conclusions are grounded in observed outcomes and which are reasoned projections, allowing proportionate rather than panicked or complacent responses. The authors argue that this evidence-differentiated approach should itself become a standard feature of AI-in-health governance, ensuring that regulatory urgency is matched to evidentiary strength.

The paper’s conclusion is deliberately double-edged. AGI integration in mental health offers the genuine possibility of democratised psychological care, extending support to billions of people who currently have none, while simultaneously posing clinical-safety and existential challenges that demand urgent ethical governance. Success, the authors contend, depends on regulatory frameworks that prioritise transparency, equity, accountability and privacy, and, crucially, on the preservation of authentic human connection as the irreplaceable core of mental health care. Machines may eventually match or exceed human clinicians in informational processing, but the therapeutic alliance, the felt experience of being understood by another conscious being, remains, on the current evidence, a uniquely human asset. Whether AGI arrives by 2030 or decades later, the review’s central message is that the window for building the ethical, legal and clinical infrastructure to receive it safely is open now, and it should not be allowed to close.

Subject of Research: The impact of artificial general intelligence on mental health

Article Title: The impact of artificial general intelligence on mental health (a systematic narrative review)

Article References: Moya, C. C., & Rodríguez, E. Q. (2026). The impact of artificial general intelligence on mental health (a systematic narrative review). Discover Mental Health. https://doi.org/10.1007/s44192-026-00589-z

Image Credits: AI Generated

DOI: 10.1007/s44192-026-00589-z

Keywords: artificial general intelligence, mental health, systematic narrative review, AI ethics, algorithmic bias, digital rights, AI-driven psychotherapy, human identity, evidence hierarchy, medico-legal accountability, algorithmic audits, existential delaborisation