Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 4 appraisals
Translational psychiatry
Multi-session CBM-I for social anxiety: examining psychopathology, cognitive, neural, and psychophysiological effects in a randomized controlled trial
Cognitive Bias Modification - Interpretation (CBM-I) aims to alter maladaptive interpretations in social anxiety, yet effects are often small and outcome measures are diverse. Although CBM-I has shown promise, its underlying mechanisms remain unclear and integration with psychophysiological and neural measures has been limited. In this randomized controlled trial, eighty-eight participants with high levels of social anxiety completed two lab sessions, an online training in between, and online follow-up. Participants filled out questionnaires, completed interpretation bias tasks, and underwent neuro-psychophysiological assessments. Active CBM-I trained positive resolutions of ambiguous social scenarios, while the sham version used neutral scenarios. The primary outcome, i.e., scores on the Liebowitz Social Anxiety Scale (LSAS), decreased across time in both groups, without group differences. However, the Brief Fear of Negative Evaluation decreased only in the active group. Interpretation bias shifted more strongly toward positive outcomes in the active group. Autonomic measures confirmed sensitivity to stress induction but did not differentiate between conditions. Electrophysiological results paralleled subjective ratings, as participants exhibited ambivalent responses to socially relevant stimuli but clearly differentiated responses toward neutral stimuli. Baseline correlations indicated strong convergence across self-report and interpretation tasks. Mediation analyses showed that reductions in negative interpretations mediated the effect of the training group on LSAS scores at follow-up. These findings identify interpretation bias as a modifiable mechanism underlying social anxiety and underscore its role as a transdiagnostic marker. Targeting interpretation bias through easily accessible and applicable online interventions may strengthen preventive and therapeutic approaches for social anxiety and related disorders.
25 May 2026
Read appraisal →JMIR formative research
Cultural Relevance and Acceptability of Cognitive Behavioral Therapy Techniques Adapted by AI or a Human Psychologist: Experimental Study
BACKGROUND: Evidence-based psychological interventions are usually not accessed by marginalized groups such as refugees. Culturally adapted psychological interventions have reported larger effect sizes than nonadapted psychological interventions. However, the cultural adaptation of interventions is a lengthy process, entailing a challenge. One potential solution to overcome this challenge is the use of artificial intelligence (AI). OBJECTIVE: The aim of this study was to investigate and compare the perceived cultural relevance and acceptability of 2 common cognitive behavioral therapy (CBT) techniques when translated and culturally adapted by AI versus a human psychologist. METHODS: In a 2×2 factorial design, the text generator type (AI vs human psychologist) and the CBT technique (cognitive restructuring vs behavior modification) were compared. CBT technique texts translated and culturally adapted either by AI or by a human psychologist were blindly rated using the Cultural Relevance Questionnaire and the Theoretical Framework of Acceptability. Raters were Arabic-speaking refugees and immigrants, aged between 18 and 69 years, residing in Sweden, Denmark, and Germany. Raters were randomly allocated to 1 of 4 conditions. Each condition consisted of 2 stimuli. Two-factor between-subject design analyses were used to analyze the data. RESULTS: A significant main effect of the text generator domain type (P=.02; η²=0.045) was found in the first rating, with texts adapted by the AI domain perceived as more culturally relevant than those adapted by the human domain. No significant main effect of the CBT technique was found in the first rating (P=.10; η²=0.022). There were no differences in the second rating. Regarding acceptability, no significant main effects of text generator domain type (P=.09; η²=0.024) or the CBT technique (P=.88; η²=0.001) were found in either of the ratings. CONCLUSIONS: CBT technique materials adapted by AI may be perceived as similarly culturally relevant as those adapted by a human psychologist. This finding implies the potential to accelerate the cultural adaptation of psychological interventions. However, AI still needs to be used with caution and in accordance with rigorous safety standards and robust frameworks.
11 May 2026
Read appraisal →JMIR mental health
It Is the Journey, Not the Destination: Moving From End Points to Trajectories When Assessing Chatbot Mental Health Safety
Large language models are rapidly becoming embedded in everyday life through artificial intelligence (AI) chatbots that people use for practical assistance and companionship, as well as for support with mental health and emotional well-being. Alongside clear benefits, clinicians and public reports increasingly describe a minority of users whose interactions seem to drift over days or weeks toward strongly questionable convictions, delusions, or suicidal crises. Importantly, clinically meaningful deterioration can occur even without overtly unsafe text outputs, via more insidious processes, such as compulsive use, sleep disruption, withdrawal from human contact, and progressive narrowing of attention around the chatbot relationship. In this Viewpoint, we argue that risk often arises not at a single tipping point but through trajectory effects that accumulate across extended dialogue and that prevailing safety evaluation approaches are misaligned with this reality because they primarily score risk at discrete conversational end points often reached through scripted dialogues lasting just a single turn or several turns. Mental health benchmarks and safety suites (including clinician-informed efforts) have advanced the field by testing refusal behavior, toxicity, and adversarial prompting. However, they often treat the last message as the unit of analysis and, therefore, miss when risk-relevant relational cues, signs of validation, contradiction handling, and shifts in certainty first emerge and how they compound. We propose that mental health safety assessment should shift from end points to trajectories by (1) treating the whole dialogue, not just the end result, as the focus of evaluation; (2) reporting turn-by-turn dynamics, such as delusion confirmation and harm enablement, and timing and persistence of safety interventions; and (3) calibrating short multiturn tests against longer, clinically realistic interaction sequences that can reveal context-length effects and drift. We further argue that transcript-only evaluation is insufficient in mental health contexts. Similar language can reflect very different internal states, and the relationship between expressed psychopathology and real-world harm is nonlinear. Therefore, safety research should incorporate proximal human outcomes following interactions (eg, shifts in certainty, openness to counterevidence, arousal, urge to continue, and subsequent sleep or behavior) and build a prospective clinical surveillance infrastructure that supports transcript donation with consent and linkage to health outcomes. Together, these steps would enable benchmarks that are clinically relevant and better aligned with the types of harms now being observed in real-world chatbot use.
7 Apr 2026
Read appraisal →JMIR Ment Health
Delusional Experiences Emerging From AI Chatbot Interactions or "AI Psychosis"
The integration of artificial intelligence (AI) into daily life has introduced unprecedented forms of human-machine interaction, prompting psychiatry to reconsider the boundaries between environment, cognition, and technology. This Viewpoint reviews the concept of "AI psychosis," which is a framework to understand how sustained engagement with conversational AI systems might trigger, amplify, or reshape psychotic experiences in vulnerable individuals. Drawing from phenomenological psychopathology, the stress-vulnerability model, cognitive theory, and digital mental health research, the paper situates AI psychosis at the intersection of predisposition and algorithmic environment. Rather than defining a new diagnostic entity, it examines how immersive and anthropomorphic AI technologies may modulate perception, belief, and affect, altering the prereflective sense of reality that grounds human experience. The argument unfolds through 4 complementary lenses. First, within the stress-vulnerability model, AI acts as a novel psychosocial stressor. Its 24-hour availability and emotional responsiveness may increase allostatic load, disturb sleep, and reinforce maladaptive appraisals. Second, the digital therapeutic alliance, a construct describing relational engagement with digital systems, is conceptualized as a double-edged mediator. While empathic design can enhance adherence and support, uncritical validation by AI systems may entrench delusional conviction or cognitive perseveration, reversing the corrective principles of cognitive-behavioral therapy for psychosis. Third, disturbances in theory of mind offer a cognitive pathway: individuals with impaired or hyperactive mentalization may project intentionality or empathy onto AI, perceiving chatbots as sentient interlocutors. This dyadic misattribution may form a "digital folie à deux," where the AI becomes a reinforcing partner in delusional elaboration. Fourth, emerging risk factors, including loneliness, trauma history, schizotypal traits, nocturnal or solitary AI use, and algorithmic reinforcement of belief-confirming content may play roles at the individual and environmental levels. Building on this synthesis, we advance a translational research agenda and five domains of action: (1) empirical studies using longitudinal and digital-phenotyping designs to quantify dose-response relationships between AI exposure, stress physiology, and psychotic symptomatology; (2) integration of digital phenomenology into clinical assessment and training; (3) embedding therapeutic design safeguards into AI systems, such as reflective prompts and "reality-testing" nudges; (4) creation of ethical and governance frameworks for AI-related psychiatric events, modeled on pharmacovigilance; and (5) development of environmental cognitive remediation, a preventive intervention aimed at strengthening contextual awareness and reanchoring experience in the physical and social world. By applying empirical rigor and therapeutic ethics to this emerging interface, clinicians, researchers, patients, and developers can transform a potential hazard into an opportunity to deepen understanding of human cognition, safeguard mental health, and promote responsible AI integration within society.
1 Dec 2025
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