The Role of Artificial Intelligence in Clinical Psychology: How AI and NLP Systems Are Reshaping Psychological Interventions. A Systematic Review

Orrù, L., & Mannarini, S. (2026). 

Clinical Psychology & Psychotherapy, 

33(2), e70242. 

https://doi.org/10.1002/cpp.70242

Abstract

Artificial Intelligence (AI) technologies are rapidly evolving and their integration into psychological practices has progressively expanded, offering new tools for diagnosis, treatment and therapeutic monitoring. This review examines the transformative role of AI, particularly Natural Language Processing (NLP) systems, in reshaping clinical psychology and digital mental health interventions (DMHIs). In particular, it explores how AI and NLP can facilitate human-machine interaction in therapy by analysing how language is used within clinical conversations and providing personalized, real-time interventions. Following PRISMA guidelines, a systematic review of literature from 2019 to 2025 identified 17 studies that met inclusion criteria, emphasizing AI's use in psychological assessment and intervention. The review focuses on two key aspects: the functions and applications of NLP-based systems in clinical practice and the advantages and benefits they offer for both psychologists and patients. Findings suggest that NLP-driven AI systems enhance both patient engagement and clinician efficiency, offering scalable, cost-effective solutions that improve access and personalization. However, challenges remain, including ethical concerns around data privacy, lack of standardization, limited generalizability across disorders and reduced human empathy. Moreover, current systems are primarily designed for well-defined conditions like anxiety and depression, with limited applicability to complex or comorbid psychological presentations. This review underscores the importance of supervised, ethically governed AI implementation. While AI holds substantial promise in augmenting clinical psychology, its success depends on maintaining human oversight, ensuring transparency and establishing shared scientific and ethical standards across the psychological community.

Summary

• Through the systematic review of the existing literature regarding the use of AI in clinical psychology, practitioners could know how AI and NLP systems are (re)shaping psychological interventions to improve them for digital mental health (DMH).
• The study describes characteristics, features and tasks of AI systems in clinical psychology, the main techniques currently adopted, from which practitioners can find information to choose the best tools for their work objectives, adapting to specific individual psychological needs.
• Practitioners could evaluate AI systems' impact on DMH promotion: beneficial effects of AI systems for patients' mental health and related challenges. Indeed, while on one hand the implementation of AI in clinical psychology has demonstrated significant improvements in psychological well-being, on the other hand some studies have reported less conclusive findings.

The unintended negative consequences of artificial intelligence use for psychologists

van Zyl, L. E. (2026).

Frontiers in Psychology, 17, Article 1729050.

https://doi.org/10.3389/fpsyg.2026.1729050

Abstract

The rapid integration of artificial intelligence (AI) into psychological practice is transforming how psychologists reason, document, and deliver care. While benefits such as improved diagnostic accuracy and administrative efficiency are widely recognized, the unintended consequences for psychologists themselves remain poorly understood. This commentary synthesizes recent empirical and conceptual literature to critically examine six domains through which sustained AI use may adversely affect psychologists’ professional functioning: (a) cognitive functioning and professional competence, (b) professional identity and meaning, (c) ethical reasoning and legal challenges, (d) interpersonal and social functioning, (e) data governance and vendor lock-in, and (f) personal wellbeing. The analysis draws on a narrative review of peer-reviewed sources from psychology, medicine, computer science, and organizational behavior, selected for their relevance to professional impacts of AI. Evidence suggests that cognitive offloading and automation bias may erode diagnostic reasoning and clinical judgment, moral deskilling and value misalignment can undermine ethical autonomy, and technostress, data dependence, and vendor lock-in pose growing threats to practitioner wellbeing and professional sovereignty. These dynamics indicate that AI operates not merely as a technical aid but as a transformative social and psychological actor that reshapes how psychologists think, decide, and relate to their work. The paper argues for the urgent development of evidence-based safeguards, practitioner education, and regulatory frameworks to ensure that AI enhances rather than erodes human competence and ethical reflection.

Here are some thoughts:

One of the more original contributions here is the concept of "AI-specific impostor syndrome." It differs from classic impostor syndrome in an important way: classic impostor syndrome involves discounting effort that was genuinely spent, while this variant arises because the effort really was minimal, so the resulting achievement feels hollow by comparison. The author grounds this in effort justification theory, which gives the idea some conceptual weight, though the supporting evidence (a finding that roughly half of AI users believe the models they use are smarter than they are) is drawn from the general population rather than from psychologists specifically. It is a theoretically motivated proposal at this point, not yet a measured clinical phenomenon.

The section on ethics and liability is likely the most practically relevant for practitioners. The "attributability gap" and "moral buffer" concepts describe a real bind: clinicians remain legally responsible for AI-influenced decisions even as they may feel less psychologically responsible for them, since the recommendation felt like it came from the system rather than their own judgment. The related "liability sink" problem is worth sitting with too, the idea that a psychologist could be exposed to liability both for using AI inappropriately and for failing to use it when it has become standard practice among peers. That double bind reflects how far regulatory and insurance frameworks currently lag behind clinical adoption.

One caveat worth keeping in mind while reading: this is a perspective commentary rather than a systematic review, and the author is upfront about that. But the six-domain structure and the density of citations can create an impression of more empirical settledness than actually exists. A fair amount of the argument extrapolates from adjacent fields, medicine, education, general knowledge work, into psychology specifically, a leap the author flags but which is easy to miss on a first read. It is best approached as a well-organized, hypothesis-generating map of where the risks might lie, rather than a set of confirmed findings.

AI in Psychotherapy: Opportunities and Risks

Neacșu, V. (2026).

Behavioral Sciences, 16(5), Article 676. 

https://doi.org/10.3390/bs16050676

Abstract

This article examines the emerging role of artificial intelligence in mental health contexts, with a particular focus on psychotherapy and the risks associated with deploying large language models (LLMs) in sensitive clinical domains. It aims to provide a broad review of current literature, highlighting key risks of general-purpose artificial intelligence (AI) systems, while also exploring the potential of clinically oriented LLMs for therapist training, supervision, and professional development. It discusses several key concerns, including AI-related psychosis, the development of parasocial attachments, and the growing number of crisis-related interactions users have with general-purpose AI models. These challenges raise important questions about the safety, reliability, and ethical management of AI systems when individuals seek support during periods of psychological crisis. Beyond identifying these risks, the article explores the potential of clinical LLMs specifically designed for mental health applications. In particular, AI can serve as a tool for therapists’ training, supervision, and professional development, offering simulated clinical scenarios, structured feedback, and support for reflective practice. The article concludes by outlining key directions for the responsible development of therapeutic AI. These include the importance of human oversight, the use of specialized and clinically informed training datasets, advances in model fine-tuning and safety alignment, and the establishment of clear professional guidelines and regulatory frameworks. Together, these developments may help ensure that AI technologies are integrated into mental healthcare in ways that prioritize safety, ethical practice, and the continued central role of human clinicians.

Here are some thoughts:

This review's main contribution is giving clinical shape to "AI psychosis," a pattern where general-purpose AI systems, optimized for agreement and engagement rather than accuracy, can amplify existing cognitive vulnerabilities in patients, particularly through persistent memory features that carry delusional themes across sessions, with OpenAI's own data suggesting roughly 630,000 users a week show signs of psychosis or mania in their conversations; the attachment framing, grounded in Bowlby and in parasocial relationship theory, offers a genuinely clinical account of why these tools can feel so compelling to isolated or attachment-insecure patients, a dynamic worth naming explicitly in informed consent and psychoeducation. 

The supervision angle is thinner but still relevant: the author flags AI tools that summarize supervisee sessions for review, and while this is framed as an efficiency gain, it carries the same risk noted throughout, namely that sensitive client material can end up uploaded to systems built for engagement rather than confidentiality or clinical accuracy, so any supervisory use warrants the same scrutiny given to patient-facing tools.

Since this is a narrative rather than systematic review, it reads best as a useful vocabulary and risk taxonomy for phenomena your patients, and possibly your supervisees, are already bringing into the room, rather than as new empirical evidence.

Divergences between Language Models and Human Brains

Zhou, Y., Liu, E., et al. (2024).

Advances in neural information 

processing systems, 37, 137999–138031.

Abstract

Do machines and humans process language in similar ways? Recent research has hinted at the affirmative, showing that human neural activity can be effectively predicted using the internal representations of language models (LMs). Although such results are thought to reflect shared computational principles between LMs and human brains, there are also clear differences in how LMs and humans represent and use language. In this work, we systematically explore the divergences between human and machine language processing by examining the differences between LM representations and human brain responses to language as measured by Magnetoencephalography (MEG) across two datasets in which subjects read and listened to narrative stories. Using an LLM-based data-driven approach, we identify two domains that LMs do not capture well: social/emotional intelligence and physical commonsense. We validate these findings with human behavioral experiments and hypothesize that the gap is due to insufficient representations of social/emotional and physical knowledge in LMs. Our results show that fine-tuning LMs on these domains can improve their alignment with human brain responses.

Here are some thoughts:

This study looked at where AI language models and human brains think differently about language. The researchers had people read Harry Potter and listen to storytelling while measuring their brain activity, then checked how well an AI model could predict those brain signals word by word. The words the AI got wrong tended to fall into two buckets: social and emotional content (feelings, relationships, tone) and physical commonsense (how objects and bodies work in the real world). When they trained the AI on these specific topics, it lined up better with human brains. The likely reason is simple: humans learn emotions and physical knowledge by living in the world and interacting with people, while AI only reads text, so those are exactly the areas where it falls short.

This is a clever and intuitive study, but I would hold the conclusions loosely. The brain data is thin (only narrative stories, and the listening portion came from just one person), so it is hard to know how far the findings generalize. The core idea that AI struggles with emotional and physical knowledge is believable and matches other research, but "the AI predicts brain signals better after fine-tuning" is a modest result, not proof that the model now understands feelings the way people do. Predicting brain activity and actually thinking like a brain are not the same thing, and the authors are honest about this limitation. So treat it as a promising hint about why AI and humans differ, not a settled answer.