arXiv subjects

Matteo Malgaroli

Publications and source records attributed to Matteo Malgaroli.

10 recordsLinked to original sources

arXiv2026

Engagement Phenotypes for a Sample of 102,684 AI Mental Health Chatbot Users and Dose-Response Associations with Clinical Outcomes

Background: Conversational AI chatbots are emerging as scalable mental health tools, but little is known about real world engagement or its relationship to clinical outcomes. Objective: To characterize engagement phenotypes among users of Ash, a purpose-built AI mental health chatbot, and examine associations with clinical change and working alliance. Methods: K-means clustering across eight behavioral features identified engagement phenotypes among 102,684 users. Subsamples completed the PHQ-9 (n=298), GAD-7 (n=298), and MSPSS (social support; n=194) baseline and 3 weeks; 11,437 users completed baseline Working Alliance Inventory (WAI). Results: Five engagement phenotypes emerged: Early Dropouts (52.2%), Power Users (1.6%), Intensive Users (4.1%), Weekly Users (25.3%), and a novel Concentrated User pattern (16.8%); across users, 66.9% had at least one overnight session (9pm-5am). Significant pre-post improvements occurred in depression (d = -0.51), anxiety (d = -0.57), and social support (d = 0.22). An observed dose-response gradient in self-reported depression improvement was replicated in a larger sample using model-predicted PHQ-9 (n = 23,813), with the largest improvements among high-engagement Power and Intensive Users (d = -0.40 and -0.43) and the smallest among Early Dropouts (d = -0.11). Higher working alliance predicted depression improvement and moderated the engagement-social support relationship. Conclusions: Engagement with AI mental health tools is multidimensional, and different clinical outcomes respond to different dimensions of use. Findings caution against treating session counts as a primary engagement metric and offer naturalistic evidence for the clinical value of purpose-built conversational AI.

cs.HC↗

arXiv2026

Talking to a Human as an Attitudinal Barrier: A Mixed Methods Evaluation of Stigma, Access, and the Appeal of AI Mental Health Support

Background: Many people who could benefit from therapy do not receive it. Conversational AI is increasingly used for mental health support, yet it is unclear which barriers AI helps mitigate. We examined whether evaluation-sensitive (shame/stigma) and structural barriers (cost/coverage/access) to psychotherapy predict perceived helpfulness of an AI mental health conversational tool (Ash), and whether effects differ by prior therapy experience or user engagement. Methods: Participants (n=395) rated Ash's helpfulness (1-5) and described barriers to therapy. Open-text responses were coded for shame/stigma, access, and cost/coverage themes. Linear regressions examined associations between barriers and perceived helpfulness, adjusting for demographics and mental health, with moderation by therapy experience. Results: Shame/stigma (B=.45, p<.001) and access barriers (B=.31, p=.020) predicted higher perceived helpfulness but cost/coverage did not (B=.13, p=.262). Prior therapy experience moderated the shame effect (interaction B=.56, p=.036): shame predicted higher helpfulness among therapy-experienced users ($\Delta$=.62, p<.001) but not therapy-naive users ($\Delta$=.03, p=.877). Among therapy-experienced participants (n=258), shame/stigma (B=.75, p<.001) and access barriers (B=.51, p=.006) predicted rating Ash more favorably. Access barriers predicted higher engagement (IRR=1.64, p<.001) and cost/coverage barriers predicted 70% more sessions (IRR=1.70, p<.001). Shame/stigma was not associated with total sessions (IRR=.80, p=.094). Conclusions: AI mental health support was perceived as most helpful by users facing shame/stigma and access barriers, particularly for therapy-experienced individuals. Access and cost barriers were most predictive of usage intensity, suggesting unmet needs. Findings highlight the importance of aligning AI tools for emotional support with user-reported barriers.

cs.HC↗

arXiv2026

Multi-Objective Alignment of Language Models for Personalized Psychotherapy

Mental health disorders affect over 1 billion people worldwide, yet access to care remains limited by workforce shortages and cost constraints. While AI systems show therapeutic promise, current alignment approaches optimize objectives independently, failing to balance patient preferences with clinical safety. We survey 335 individuals with lived mental health experience to collect preference rankings across therapeutic dimensions, then develop a multi-objective alignment framework using direct preference optimization. We train reward models for six criteria -- empathy, safety, active listening, self-motivated change, trust/rapport, and patient autonomy -- and systematically compare multi-objective approaches against single-objective optimization, supervised fine-tuning, and parameter merging. Multi-objective DPO (MODPO) achieves superior balance (77.6% empathy, 62.6% safety) compared to single-objective optimization (93.6% empathy, 47.8% safety), and therapeutic criteria outperform general communication principles by 17.2%. Blinded clinician evaluation confirms MODPO is consistently preferred, with LLM-evaluator agreement comparable to inter-clinician reliability.

cs.LG↗

arXiv2026

Beyond Simulations: What 20,000 Real Conversations Reveal About Mental Health AI Safety

Mental-health AI safety is typically evaluated with small, simulation-based benchmarks that may not reflect the linguistic and contextual diversity of deployment. We pair four benchmark replications with an ecological audit of real-world conversations to evaluate a purpose-built mental-health AI alongside six frontier general-purpose models spanning four families (OpenAI GPT-5, GPT-5.1, GPT-5.2; DeepSeek V3; Google Gemini 3 Flash; Moonshot Kimi K2). The purpose-built system produced significantly lower overall potentially harmful content rates than every frontier comparator on suicide/self-harm, eating-disorder, and substance-use prompts (CCDH Benchmark: Ash 6.2% vs frontier models 18.0-52.0%, all p < .001). In an audit of 20,000 deployment conversations, clinician review within the audit pipeline confirmed no suicide-risk conversations lacking crisis resources and three NSSI-related conversations without crisis intervention, a within-pipeline conditional rate of 3/800 (0.38%). To extend population-level inference, we adjudicated 600 conversations randomly sampled from the full deployment distribution via blinded clinician review. Against these labels, the LLM judge showed 100% sensitivity (6/6; 95% CI 54.1-100%) and 99.2% specificity (589/594) at its operating threshold, and the conversational model delivered crisis resources in all 6 clinician-confirmed cases (zero end-to-end false negatives; rule-of-three upper 95% CI 0.50%). Findings argue for ecological auditing as a complement to pre-deployment evaluation in mental-health AI safety

cs.CY↗

arXiv2026

Language Markers of Emotion Flexibility Predict Depression and Anxiety Treatment Outcomes

Predicting treatment non-response for anxiety and depression is challenging, in part because of sparse symptom assessments in real-world care. We examined whether passively captured, fine-grained emotions serve as linguistic markers of treatment outcomes by analyzing 12 weeks of de-identified teletherapy transcripts from 12,043 U.S. patients with moderate-to-severe anxiety and depression symptoms. A transformer-based small language model extracted patients' emotions at the talk-turn level; a state-space model (VISTA-SSM) clustered subgroups based on emotion dynamics over time and produced temporal networks. Two groups emerged: an improving group (n=8,230) and a non-response group (n=3,813) showing increased odds of symptom deterioration, and lower likelihood of clinically significant improvement. Temporal networks indicated that sadness and fear exerted most influence on emotion dynamics in non-responders, whereas improving patients showed balanced joy, sadness, and neutral expressions. Findings suggest that linguistic markers of emotional inflexibility can serve as scalable, interpretable, and theoretically grounded indicators for treatment risk stratification.

cs.CL↗

arXiv2025

DIAL: Direct Iterative Adversarial Learning for Realistic Multi-Turn Dialogue Simulation

Realistic user simulation is crucial for training and evaluating multi-turn dialogue systems, yet creating simulators that accurately replicate human behavior remains a significant challenge. An effective simulator must expose the failure modes of the systems under evaluation. This work introduces Direct Iterative Adversarial Learning (DIAL), an adversarial framework that iteratively enhances user simulator realism through a competitive dynamic between a generator (user simulator) and a discriminator. When applied to mental health support, a domain characterized by diverse failure types and a critical dependence on realistic user behavior for failure detection, DIAL restores lexical diversity diminished by supervised fine-tuning and drastically reduces discriminator accuracy. The resulting simulator exhibits a strong correlation between simulated and real failure occurrence rates while maintaining low distributional divergence of failure modes. These findings indicate that DIAL is a promising method for developing realistic user simulators in multi-turn dialogue, facilitating reliable and cost-effective system evaluation prior to deployment.

cs.CL↗

arXiv2025

Generative AI Purpose-built for Social and Mental Health: A Real-World Pilot

Generative artificial intelligence (GAI) chatbots built for mental health could deliver safe, personalized, and scalable mental health support. We evaluate a foundation model designed for mental health. Adults completed mental health measures while engaging with the chatbot between May 15, 2025 and September 15, 2025. Users completed an opt-in consent, demographic information, mental health symptoms, social connection, and self-identified goals. Measures were repeated every two weeks up to 6 weeks, and a final follow-up at 10 weeks. Analyses included effect sizes, and growth mixture models to identify participant groups and their characteristic engagement, severity, and demographic factors. Users demonstrated significant reductions in PHQ-9 and GAD-7 that were sustained at follow-up. Significant improvements in Hope, Behavioral Activation, Social Interaction, Loneliness, and Perceived Social Support were observed throughout and maintained at 10 week follow-up. Engagement was high and predicted outcomes. Working alliance was comparable to traditional care and predicted outcomes. Automated safety guardrails functioned as designed, with 76 sessions flagged for risk and all handled according to escalation policies. This single arm naturalistic observational study provides initial evidence that a GAI foundation model for mental health can deliver accessible, engaging, effective, and safe mental health support. These results lend support to findings from early randomized designs and offer promise for future study of mental health GAI in real world settings.

cs.CY↗

arXiv2024

VISTA-SSM: Varying and Irregular Sampling Time-series Analysis via State Space Models

We introduce VISTA, a clustering approach for multivariate and irregularly sampled time series based on a parametric state space mixture model. VISTA is specifically designed for the unsupervised identification of groups in datasets originating from healthcare and psychology where such sampling issues are commonplace. Our approach adapts linear Gaussian state space models (LGSSMs) to provide a flexible parametric framework for fitting a wide range of time series dynamics. The clustering approach itself is based on the assumption that the population can be represented as a mixture of a fixed number of LGSSMs. VISTA's model formulation allows for an explicit derivation of the log-likelihood function, from which we develop an expectation-maximization scheme for fitting model parameters to the observed data samples. Our algorithmic implementation is designed to handle populations of multivariate time series that can exhibit large changes in sampling rate as well as irregular sampling. We evaluate the versatility and accuracy of our approach on simulated and real-world datasets, including demographic trends, wearable sensor data, epidemiological time series, and ecological momentary assessments. Our results indicate that VISTA outperforms most comparable standard times series clustering methods. We provide an open-source implementation of VISTA in Python.

stat.AP↗

arXiv2024

Can AI Relate: Testing Large Language Model Response for Mental Health Support

Large language models (LLMs) are already being piloted for clinical use in hospital systems like NYU Langone, Dana-Farber and the NHS. A proposed deployment use case is psychotherapy, where a LLM-powered chatbot can treat a patient undergoing a mental health crisis. Deployment of LLMs for mental health response could hypothetically broaden access to psychotherapy and provide new possibilities for personalizing care. However, recent high-profile failures, like damaging dieting advice offered by the Tessa chatbot to patients with eating disorders, have led to doubt about their reliability in high-stakes and safety-critical settings. In this work, we develop an evaluation framework for determining whether LLM response is a viable and ethical path forward for the automation of mental health treatment. Our framework measures equity in empathy and adherence of LLM responses to motivational interviewing theory. Using human evaluation with trained clinicians and automatic quality-of-care metrics grounded in psychology research, we compare the responses provided by peer-to-peer responders to those provided by a state-of-the-art LLM. We show that LLMs like GPT-4 use implicit and explicit cues to infer patient demographics like race. We then show that there are statistically significant discrepancies between patient subgroups: Responses to Black posters consistently have lower empathy than for any other demographic group (2%-13% lower than the control group). Promisingly, we do find that the manner in which responses are generated significantly impacts the quality of the response. We conclude by proposing safety guidelines for the potential deployment of LLMs for mental health response.

cs.CL↗

arXiv2023

The Capability of Large Language Models to Measure Psychiatric Functioning

The current work investigates the capability of Large language models (LLMs) that are explicitly trained on large corpuses of medical knowledge (Med-PaLM 2) to predict psychiatric functioning from patient interviews and clinical descriptions without being trained to do so. To assess this, n = 145 depression and n =115 PTSD assessments and n = 46 clinical case studies across high prevalence/high comorbidity disorders (Depressive, Anxiety, Psychotic, trauma and stress, Addictive disorders) were analyzed using prompts to extract estimated clinical scores and diagnoses. Results demonstrate that Med-PaLM 2 is capable of assessing psychiatric functioning across a range of psychiatric conditions with the strongest performance being the prediction of depression scores based on standardized assessments (Accuracy range= 0.80 - 0.84) which were statistically indistinguishable from human clinical raters t(1,144) = 1.20; p = 0.23. Results show the potential for general clinical language models to flexibly predict psychiatric risk based on free descriptions of functioning from both patients and clinicians.

cs.CL↗