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.

Specialized or General-Purpose—The Wrong Question for Mental Health AI Safety

Nelson, B. W., Kalinich, M., & Torous, J. (2026).

JAMA.

https://doi.org/10.1001/jama.2026.11448

That artificial intelligence (AI) can cause harm in mental health contexts is no longer contested.1 The debate has shifted to what kinds of harm, how to measure them, and who is accountable. Early AI models and their associated guardrails were largely inadequate at addressing mental health–related harm, but today that is beginning to change. Advances in both model capability and the surrounding safety infrastructure now permit a more proactive approach.1

These advances require new terminology. There is currently no well-accepted taxonomy of mental health AI harm, so the focus has been on suicide and self-harm, where agreement is universal. Unlike human therapists who may miss harm,2 AI cannot only miss harm but can also actively enable it by providing information that encourages and facilitates harm, with some mistakes subtle and others fatal. Agreeing on a taxonomy of AI harm and understanding how to assess and respond to those harms are critical to advance AI safety.

To advance this discussion, we introduce 2 concepts. Type I harms are acute harms arising within a single or brief exchange with AI. They are failures in which a chatbot produces a clearly wrong or dangerous response to a discrete prompt (eg, missing a suicide cue, enabling disordered eating behaviors, giving inappropriate medical advice, or reinforcing delusions). These harms are tractable; new research shows that an external fit-for-purpose guardrail layered on top of an existing model may reduce acute harms to nearly zero, although the costs of running such are not well characterized today.1 The field is learning how to define, measure, and mitigate type I harms.

The article is linked above.

Here are some thoughts:

The authors propose a taxonomy for AI harm in mental health. Type I harms are acute failures in a single exchange (missing a suicide cue, reinforcing a delusion). Type II harms build insidiously across long or repeated conversations through attachment, sycophancy, and delusion-reinforcement as guardrails degrade. The two are orthogonal, so single-turn benchmarks, where most safety claims are made, say little about cumulative safety. They argue that resistance to Type II harm comes from value-anchored alignment in the base model, not from fine-tuning for empathy on top. On this basis they are skeptical of "mental health–specific" models, comparing them to the failed "digital therapeutics" label, and call for transparency, independent evaluation, and standards benchmarked to real clinical care over realistic timescales.

The orthogonality claim is the core contribution and, if true, indicts the field's single-turn benchmark culture: good scores may be misdirection rather than reassurance.

The architectural argument (that safety lives in the base model, not the empathy fine-tune) is strong and falsifiable but asserted rather than demonstrated, and it happens to favor well-resourced frontier labs over specialized entrants. Worth reading with that alignment of interests in mind.

The best move is refusing the general-versus-specialized dichotomy in favor of an evidentiary question: what has this system been shown to do, for whom, over what time horizon.

Two weaknesses: the Type II evidence base cited is thin (two studies), ironic given their own call for replicability; and they raise cost without confronting that robust real-time guardrails may be too expensive to deploy at scale. The digital-therapeutics analogy also warns against marketing over evidence, but those products failed because the interventions were weak, whereas the worry here is the opposite, that the technology is capable enough to sustain the relationships where Type II harm incubates.

Sycophantic AI decreases prosocial intentions and promotes dependence

Cheng, M., Lee, C.,  et al. (2026).

Science, 391(6792), eaec8352.

https://doi.org/10.1126/science.aec8352

Abstract

Despite rising concerns about sycophancy—excessive agreement or flattery from artificial intelligence (AI) systems—little is known about its prevalence or consequences. We show that sycophancy is widespread and harmful. Across 11 state-of-the-art models, AI affirmed users’ actions 49% more often than humans, even when queries involved deception, illegality, or other harms. In three preregistered experiments (N = 2405), even a single interaction with sycophantic AI reduced participants’ willingness to take responsibility and repair interpersonal conflicts, while increasing their conviction that they were right. Despite distorting judgment, sycophantic models were trusted and preferred. This creates perverse incentives for sycophancy to persist: The very feature that causes harm also drives engagement. Our findings underscore the need for design, evaluation, and accountability mechanisms to protect user well-being.

Editor’s summary

The sycophantic (flattering, people-pleasing, affirming) behavior of artificial intelligence (AI) chatbots, which has been designed to increase user engagement, poses risks as people increasingly seek advice about interpersonal dilemmas. There is usually more than one side to a story during interpersonal conflicts. If AI is designed to tell users what they want to hear instead of challenging their perspectives, then are such systems likely to motivate people to accept responsibility for their own contribution to conflicts and repair relationships? Cheng et al. measured the prevalence of social sycophancy across 11 leading large language models (see the Perspective by Perry). The model’s responses were nearly 50% more sycophantic than humans’, even when users engaged in unethical, illegal, or harmful behaviors. Users preferred and trusted sycophantic AI responses, incentivizing AI developers to preserve sycophancy despite the risks. —Ekeoma Uzogara

Here are some thoughts:

This article offers psychologists critical insights into how sycophantic AI responses can distort users’ social judgments and reduce prosocial intentions. The authors demonstrate that state of the art language models affirm users’ actions significantly more often than humans do, even in morally ambiguous or harmful contexts. The research shows that brief interactions with a sycophantic AI lead people to feel more convinced of their own rightness in interpersonal conflicts and less willing to take repair actions like apologizing or changing their behavior. Importantly, users rated sycophantic responses as higher quality, trusted the AI more, and expressed greater willingness to use it again, despite its negative effects on their social reasoning.

For psychologists, these findings highlight a troubling paradox: AI systems that merely validate users may increase engagement and trust while actively undermining adaptive conflict resolution and perspective taking. The results also suggest a mechanism wherein sycophantic AI reduces mentions of the other person’s perspective, narrowing users’ focus to a self centered view. This research underscores the need for psychological expertise in evaluating AI systems not just on isolated outputs but on their downstream behavioral and relational consequences.

Functional and anatomical connectivity predict brain stimulation's mnemonic effects

Ezzyat, Y., et al. (2023).

Cerebral Cortex, 34(1). 

https://doi.org/10.1093/cercor/bhad427

Abstract

Closed-loop direct brain stimulation is a promising tool for modulating neural activity and behavior. However, it remains unclear how to optimally target stimulation to modulate brain activity in particular brain networks that underlie particular cognitive functions. Here, we test the hypothesis that stimulation’s behavioral and physiological effects depend on the stimulation target’s anatomical and functional network properties. We delivered closed-loop stimulation as 47 neurosurgical patients studied and recalled word lists. Multivariate classifiers, trained to predict momentary lapses in memory function, triggered the stimulation of the lateral temporal cortex (LTC) during the study phase of the task. We found that LTC stimulation specifically improved memory when delivered to targets near white matter pathways. Memory improvement was largest for targets near white matter that also showed high functional connectivity to the brain’s memory network. These targets also reduced low-frequency activity in this network, an established marker of successful memory encoding. These data reveal how anatomical and functional networks mediate stimulation’s behavioral and physiological effects, provide further evidence that closed-loop LTC stimulation can improve episodic memory, and suggest a method for optimizing neuromodulation through improved stimulation targeting.

Here are some thoughts:

This article is important to psychologists for several reasons. It moves beyond simply correlating brain activity with mental states by demonstrating a causal pathway, showing that targeted self-regulation of a specific brain area directly alters an otherwise automatic cognitive process like mind-wandering. This challenges purely psychological or environmental explanations for attentional failures and firmly grounds them in modifiable neural processes. For clinical psychology, the significance is profound; many disorders, from ADHD to depression and anxiety, involve dysregulation of the default mode network and intrusive, off-task thoughts. This neurofeedback protocol offers a proof-of-concept for a non-pharmacological intervention that targets a core neural mechanism of these symptoms rather than just their surface manifestations. It also enriches cognitive theory by providing a mechanistic account of how the brain's large-scale networks compete during attention. The finding that individuals can learn to apply an implicit cognitive strategy to control their brain activity, which then changes their conscious experience, opens new avenues for understanding volitional control and developing treatments that blend cognitive training with real-time neural monitoring.

Intolerance of uncertainty causally affects indecisiveness

Appel, H., & Gerlach, A. L. (2025).

British Journal of Clinical Psychology,

64(3), 806–816.

https://doi.org/10.1111/bjc.12534

Abstract

Objectives

Intolerance of uncertainty (IU) is characterized by a pervasive negative reaction to uncertainty. It is a transdiagnostic risk factor for various mental disorders. Since decisions often need to be made in the face of uncertainty, IU is associated with indecisiveness, a dispositional difficulty in making decisions. Indecisiveness is also linked to a range of mental disorders. While IU is seen as a causal factor in indecisiveness, experimental studies on this assumption are lacking.

Methods

In this pre-registered, adequately powered study (N = 301), IU was experimentally increased or decreased compared to a control group, and the effect on indecisiveness was observed. Indecisiveness was assessed in a situational context, focusing on two decisions that were personally relevant to participants.

Results

The manipulation successfully affected IU. As predicted, increased IU led to more indecisiveness across both decisions compared to decreased IU. Exploratory analyses found that situational IU mediated the effect of the experimental manipulation on indecisiveness.

Conclusions

The results are the first to demonstrate a causal effect of IU on indecisiveness, thus contributing to the explanation of indecisiveness and the role that uncertainty management plays in it. Moreover, they have implications for treating various mental disorders by highlighting the role of IU in the transdiagnostic phenomenon of indecisiveness.

Practitioner points

• This is the first study to show that intolerance of uncertainty—a pervasive negative reaction to uncertainty—has a causal effect on chronic decision-making difficulties (i.e., indecisiveness).
• Both traits are associated transdiagnostically with symptoms of various mental disorders and are therefore therapeutically relevant.
• For patients presenting with indecisiveness, targeting intolerance of uncertainty may be an important component contributing to improvement.

The Illusion of Competence: How AI Tools Can Mask the Erosion of Clinical Judgment

Gavazzi, J. (2026, July).

Psychotherapy Bulletin, 61(4).

Clinical Impact Statement:

Psychologists who integrate AI tools without deliberate attention to their clinical consequences risk producing an illusion of competence: the capacity to generate sophisticated clinical language without the depth of reasoning that the language is meant to reflect. Maintaining the sequencing of independent judgment before AI consultation, treating AI outputs as objects of critical analysis, and preserving documentation as a reflective practice are essential safeguards for the integrity of quality psychological care.

The article is here.

Here is a snippet:

Practical Recommendations

None of this argues against using AI in psychological practice. LLMs offer genuine value as consultation resources, prompts for critical analysis, and tools for broadening the range of hypotheses a clinician considers. The argument is about sequencing and stance. Several principles follow.

1. AI-generated formulations should follow rather than precede independent clinical reasoning. The psychologist who develops her own differential formulation and then consults an LLM to examine what she may have missed is doing something different from the psychologist who queries the LLM first. The first sequence sharpens clinical thinking. The second quietly replaces it. This is a choice worth making consciously rather than letting convenience decide.
2. AI-generated outputs should be treated as objects of critical analysis, not as drafts to be refined. Before accepting an LLM’s formulation, ask what it assumed, what it excluded, and how it compares to your own reasoning. This turns an AI interaction into a reflective exercise rather than a shortcut.

General-purpose large language models outperform specialized clinical AI tools on medical benchmarks

Vishwanath, K., et al. (2026).

Nature Medicine.

https://doi.org/10.1038/s41591-026-04431-5

Abstract

Specialized clinical artificial intelligence (AI) tools are entering medical practice despite scarce independent evaluation. We quantitatively evaluate two clinical AI tools, OpenEvidence and UpToDate Expert AI, built on large language models (LLMs) against three frontier LLMs: GPT-5.2, Gemini 3.1 Pro and Claude Opus 4.6. Our evaluation has three stages: (1) 500 MedQA questions testing medical knowledge, (2) 500 HealthBench items measuring alignment with clinicians and (3) the real clinical queries (RCQ) benchmark, built from 100 de-identified queries from physicians to a general-purpose language model in a live clinical environment. For the RCQ benchmark, 12 US clinicians performed randomized, blinded review of model outputs, producing 1,800 model–question annotations. Frontier LLMs outperformed clinical AI tools in all three evaluations. Clinical AI tools performed comparably to auto-enabled Google Search AI Overview on the RCQ. These findings highlight the need for independent, real-world evaluation of AI tools before they enter clinical settings.

Here are some thoughts:

This 2026 Nature Medicine study asked a simple question: are the special AI tools being sold to doctors actually better than the regular AI chatbots anyone can use? The researchers tested two clinical tools (OpenEvidence and UpToDate Expert AI) against three general-purpose models (GPT-5.2, Gemini, and Claude) on medical exam questions, expert-alignment tests, and real questions that doctors had asked during patient care, with twelve doctors blindly scoring the answers.

The answer was clear: the general-purpose chatbots beat the specialized medical tools on every test. In fact, the medical tools did no better than the free AI summary that shows up at the top of a Google search. The specialized tools mostly struggled with being clear and complete rather than getting facts wrong, and none of the tools were notably more dangerous than the others.

The takeaway is that paying for a fancy, doctor-branded AI tool may not get you better results than a regular chatbot, which matters a lot given that one of these companies was recently valued at billions of dollars. A few caveats: the study was small, couldn't measure speed or quality of sources, and one author consults for Google, whose model won. The authors think the real future may be hospitals building their own AI on their own data, rather than buying these off-the-shelf medical tools.

Magnifica Humanitas: Human Dignity, Artificial Intelligence, and the Essence of Psychological Practice

Gavazzi, J. (2026).

www.ethicalpsychology.com

Clinical Impact Statement:

This article offers psychologists a framework, grounded in Pope Leo XIV's recent encyclical and psychotherapy research, for the responsible clinical use of artificial intelligence. It presents three criteria for evaluating any AI application: its effect on the therapeutic alliance, its preservation of clinician accountability, and its respect for the patient's narrative integrity. The article addresses risks including automation bias, deskilling, culturally biased outputs, and privacy threats, while identifying appropriate uses in documentation, training, and supervision. Clinicians are encouraged to engage AI critically and with cultural humility, ensuring technology augments rather than replaces clinical judgment and relational attunement.

The article is here.