Introduction: Serious games constitute a relevant subject of research in the aim of making psychological digital treatment increasingly engaging, accessible and resource-efficient. It is thus motivated to investigate if a serious game administered in an ICBT milieu can reduce depression in participants consuming antidepressants.

Methods: Participants (18+ years with a stable medical treatment for depression) received access to an unguided intervention consisting of the CBT-based serious smartphone game Fig: The Game for Depression and 25 optional ICBT modules with on-demand therapist support for a duration of 9 weeks, or assignment to a waitlisted control condition. The primary outcome was depression symptomatology (MADRS-S). Secondary outcomes were anxiety symptomatology (GAD-7), medication adherence (MMAS-8) and quality of life (BBQ). Outcomes were analysed using linear regression models considering pre-treatment scores as a continuous covariate and assigned condition as a binary covariate, utilising using full information maximum likelihood estimation (FIML) to handle missing data.

Results: Regression models showed significant differences between the treatment and control conditions regarding depression (d = 0.74) and anxiety (d = 0.67) symptomatology as well as medication adherence (d = 0.46) at post-treatment, favouring the treatment condition. No significant difference was found regarding quality of life. Most participants (79 %) installed Fig. Therapists were contacted by 31.6 % of participants, and 27.6 % answered ≥1 ICBT worksheet throughout treatment.

Conclusions: The results indicate that Fig can be administered as a resource-efficient adjunct to care consisting of antidepressants, access to on-demand therapist contact and ICBT modules, to reduce depression and anxiety symptoms as well as increase medication adherence. Still, non-significant changes in quality of life indicate that there may be additional needs that were not fully met by the studied intervention.

Background: The developments in generative artificial intelligence (GenAI) are expected to have a substantial impact on the field of mental health, including the delivery of clinical services and the role of psychologists. However, there is limited knowledge on how psychologists use and perceive GenAI, including its potential, risks and effects on the future psychologist role and professional practice. This includes competency levels among psychologists and factors associated with the intention to use GenAI within the psychology profession.

Objective: This study examined the use of digital technology and GenAI, perceived competencies, behavioral intention to use GenAI, and perceptions of its future impact on professional practice among psychology students, internship psychologists, and licensed psychologists in Sweden.

Methods: A national cross-sectional online survey was conducted in January-February 2026 in collaboration with the Swedish Psychological Association. The 36-item survey included closed and open-ended questions, including Likert-scale, nominal scale, and multiple-choice items. A total of 1077 respondents, with 64.7% (n = 697) being women and with mean age of 45.5 years (SD = 13.7), were included in the analyses. Responses were analyzed using descriptive and inferential statistical methods, including hierarchical multiple linear regression, analysis of variance (ANOVA), and correlational analysis.

Results: Approximately one in three psychologists (30.5%, 282/990) reported frequent (daily or weekly) professional use of GenAI, and nearly half (46.6%, 461/990) reported frequent private use, with private and professional use strongly associated (r = .62, P < .001). The most common professional applications of GenAI were information retrieval and knowledge acquisition, text summarization, and drafting, editing, or proofreading. Almost half of psychologist (44.6%, 442/990) reported that their patients or clients had used GenAI for mental health purposes. Responses indicated high levels of general and professional digital competence, whereas lower levels were reported for general and professional GenAI competence. Attitudes toward future professional integration of GenAI were driven primarily by current private and professional GenAI use. The Unified Theory of Acceptance and Use of Technology (UTAUT) variables explained 73.3% of the variance in behavioral intention to use GenAI professionally, with curiosity (β = .44) and performance expectancy (β = .32) emerging as the strongest predictors.

Conclusions: Psychologists are already integrating GenAI in their everyday work, utilizing multipurpose solutions they are familiar with from private use. However, more than one-third have never used it professionally, indicating an emerging digital divide. The high degree of GenAI use among patients and lack of GenAI expertise highlight the need for increased competencies and further examination of the role of GenAI in the future development of the psychologist role and practice. As experience and curiosity towards GenAI were the strongest correlates of both adoption intention and attitudes toward integration into future professional practice, this should be explored in future research and competency development.

The rapid evolution of large language models has accelerated the development of agentic artificial intelligence (AI) systems capable of pursuing autonomous goals, creating an urgent need for structural frameworks in psychiatry and psychotherapy. While existing classifications often draw parallels to autonomous driving, this paper argues that the mental health domain requires a distinct, domain-specific theoretical foundation, as the 2 domains differ fundamentally in their semantic, ideographic, and epistemological demands. Furthermore, they differ in their end goals, for which we introduce terms such as agentic guidance capability. To guide clinicians and researchers through these developments, we propose a 5-stage taxonomy for language-based AI systems that differentiates technical functionality from clinical effectiveness. The taxonomy progresses from level 1 (knowledge level), in which systems perform static benchmark tasks, to level 2 (elementary level), characterized by dynamic engagement in specific therapeutic microskills. At level 3 (integration level), systems achieve consistency across and within modules, as well as basic case-level conceptualization suitable for blended therapy under human oversight. Level 4 (saturation level) describes therapist-in-the-loop systems capable of autonomous functioning with minimal supervision, whereas level 5 (mastery level) represents AI systems that are technically capable of performing autonomous therapy. By distinguishing technical functionality from clinical effectiveness, we conclude that level 4 or level 5 performance does not automatically translate into full treatment effectiveness, even if high treatment fidelity can be achieved. We conclude by emphasizing the need to shift benchmarking from static knowledge tests to dynamic evaluations of therapeutic capabilities in order to safely navigate the transition toward autonomous care.

Internet-delivered psychological treatment has moved from self-help books to guided online programs and now to systems that generate their own therapeutic language. Automation is advancing both inside the research field and outside it, where many people already use general-purpose chatbots for support off-label, without trials or oversight.

This keynote asks what can be delegated. In guided internet treatment the therapist's contribution is compressed into a few minutes of written feedback made up of observable, codable behaviors, a description that doubles as a specification for machines and as a way to audit them. Emerging trials make AI-generated and human feedback hard to tell apart, on outcomes and on the relationship alike, but equivalence is easier to claim than to demonstrate. Safety separates cleanly from capability: supervised systems have behaved well, while unsupervised companion bots are another matter.

Using a five-stage taxonomy of language-based systems, I argue that technical capability and clinical effectiveness are separate axes, and that fluency at a higher level does not entail a treatment effect. The real question is therefore not whether AI can replace therapists, but which functions can be delegated, at what level of autonomy, under whose oversight, and disclosed to whom.

Artificial intelligence (AI) offers a potential solution to the scalability limits of internet-based psychological interventions. This randomized controlled trial evaluated two smartphone-based, AI-delivered interventions for social anxiety disorder (SAD): Psychodynamic Therapy (AI-PDT) and Cognitive Behavioral Therapy (AI-CBT). One hundred and two adults with SAD were randomized (1:1:1) to AI-PDT, AI-CBT, or a waitlist control for a 4-week daily intervention. The primary outcome was social anxiety severity measured by the Social Phobia Inventory, and analyzed using Linear Mixed Models. Both interventions yielded significant, moderate reductions in symptoms from baseline to post-treatment, with within group effect sizes of d = 0.79 (AI-PDT) and d = 0.72 (AI-CBT), and with no significant differences observed between the two active treatments. However, the between-group difference against the waitlist control at post-treatment was not significant (AI-PDT: p = .204, d = 0.43; AI-CBT: p = .727, d = 0.18), possibly due to substantial improvement in the waitlist condition (d = 0.54). At 1-month follow-up, the difference widened: AI-PDT became significantly superior to waitlist (d = 0.65), while AI-CBT did not (d = 0.51). Therapeutic alliance was established early in both conditions and remained stable throughout treatment, suggesting that AI-delivered interventions can foster a therapeutic alliance regardless of theoretical orientation. Secondary regression analysis revealed that a co-occurring diagnosis of ADHD or autism spectrum disorder significantly predicted poorer outcomes. These findings suggest that the comparative efficacy of AI-guided interventions may increase over time and are capable of fostering moderate symptom reductions, though future iterations require adaptation to better support neurodivergent users.

Introduction: Clinical guidelines for the treatment for obsessive-compulsive disorder (OCD) have remained unchanged since 2005, wherein gold-standard treatment centers around voluntary exposure to feared stimuli and disengagement of safety-seeking behaviors. While effective evidence-based treatments exist, many patients do not receive them due to barriers such as limited availability of specialized therapists, stigma, long waiting lists, and high costs. Furthermore, it is common for sufferers of OCD to present to treatment more than a decade after initial symptom onset. It is thus imperative to seek ways to break some of the barriers to treatment and deliver gold-standard therapy earlier to sufferers. This presentation will showcaseZeroOCD, a novel AR-CBT app-based intervention which was designed to target these barriers, and provide preliminary insights from a randomized controlled trial.

Methods: The ZeroOCD project is an ongoing international collaboration between institutions in Sweden, Switzerland, the Netherlands, and Belgium. The ZeroOCD app combines augmented reality (AR) exposure therapy and CBT principles to ameliorate OCD symptoms, with a focus on contamination-related symptoms (e.g., fear of dirt, germs, bodily fluids). It offers a module-based treatment program featuring evidence-based tools, including psychoeducation, immersive AR stimuli (both realistic and ambiguous to address disgust and uncertainty intolerance), in vitro and in vivo exposure and response prevention, and relapse prevention. AR allows for exposure exercises in the patient's natural home environment, bridging the gap between clinical settings and real-world triggers.

Results: The presentation will provide preliminary insights from the active treatment arms of the ZeroOCD project (currently recruiting). It will showcase the app's functionality and user environment, and delineate the specific mechanisms of change targeted by the intervention regarding contamination-related symptoms. As such, this presentation provides attendees with a unique opportunity to better understand the future of AR-CBT interventions generally, and the future of accessible OCD interventions.

Imagine that every week you write to your therapist, and someone writes back, warm, attentive, and helpful. Could you tell whether it was an AI? And if you could not, would it matter? We put both questions to the test.

Most people who need psychological treatment never receive it, so this is not a thought experiment for long: when the clinic is closed, people already turn to whatever answers. I take three common objections in turn. That it does not work: across many controlled trials, symptoms come down modestly but reliably. That it is a cold machine: in our trial of internet-based CBT for social anxiety, participants trusted humans more beforehand, yet the bond formed with the AI was at least as strong, and neither who actually wrote the feedback nor what people were told about it changed how much they improved. That it is dangerous: under professional review it was not, while unsupervised companion bots are a different matter entirely.

A second trial, in which the therapy was delivered by AI alone, found benefits that continued to grow after treatment ended. But short trials with willing volunteers and supervised systems support a narrow claim, and no difference found is not the same as proven equal. The useful question is not replacement but combination: reach from the machine, judgment and responsibility from the human.

There is little consensus on the optimal components of gambling psychological treatments. This study aimed to identify clinical consensus statements regarding the perceived effectiveness of gambling intervention content (change techniques, participant/recruitment characteristics, delivery characteristics, and evaluation characteristics) from a panel of researchers with psychological gambling treatment expertise across 11 countries. A two-round modified Delphi study was conducted. Thirty-five panellists rated the perceived effectiveness of 96 gambling intervention components for achieving clinically helpful change, which was defined as “reduction in gambling severity, expenditure, and frequency”. Consensus criteria on effectiveness and ineffectiveness were defined a priori. Consensus statements were identified for four of 19 change techniques (motivational enhancement, relapse prevention, cognitive restructuring, and plan social support), five of 23 participant/recruitment characteristics (e.g. eligibility screening took place), 17 of 47 delivery characteristics (e.g. the therapy goal was to reduce time and/or money spent gambling), and three of seven evaluation characteristics (e.g. specific process or mediators are targeted by the intervention). These statements, when interpreted with consideration of contextual factors, can inform the selection of likely effective components to employ in gambling treatment programs and indicate where future research efforts may be most beneficial.

Over recent decades, mental healthcare reforms have been proposed to facilitate deinstitutionalization, integration into primary care, task-sharing to non-specialist providers and, more recently, digital interventions. All are aimed at improving the accessibility, acceptability and effectiveness of care. However, many healthcare systems still suffer from complexity, rigidity and inequity in access. New and more integrated models of service delivery are needed to fully harness the potential of evidence-based approaches in mental healthcare, including digital or community-based interventions. The current contribution provides an overview of recent developments - in the organization of care, allocation of healthcare services, and the digital transformation of care - and presents the Comprehensive Model for Mental health Access and service use (CoMMA). The process model includes both informal support (e.g., self-help and community care) as well as formal services (e.g., diagnostics and interventions delivered by healthcare professionals). In line with the increasing digital transformation of care, CoMMA also addressed how technology can play a role in the different model components. The purpose of the model is to provide guidance to healthcare systems, professionals and trainees in shaping the provision of evidence-based psychological services and implementing interventions. It hereby aims to complement ongoing societal, regulatory, and economic changes in the healthcare field by providing a conceptual and substantive narrative. The model shows how mental health services can be organized based on current scientific frameworks, policy perspectives, and clinical practice.