arXiv subjects

Veith Weilnhammer

Publications and source records attributed to Veith Weilnhammer.

3 recordsLinked to original sources

arXiv2026

One-shot emergency psychiatric triage across 15 frontier AI chatbots

AI chatbots are increasingly used for health advice, but their performance in psychiatric triage remains undercharacterized. Psychiatric triage is particularly challenging because urgency must often be inferred from thoughts, behavior, and context rather than from objective findings. We evaluated the performance of 15 frontier AI chatbots on psychiatric triage from realistic single-message disclosures using 112 clinical vignettes, each paired with 1 of 4 original benchmark triage labels: A, routine; B, assessment within 1 week; C, assessment within 24 to 48 hours; and D, emergency care now. Vignettes covered 9 psychiatric presentation clusters and 9 focal risk dimensions, organized into 28 presentation-by-risk groups. Each group contributed 4 distinct vignettes, with 1 vignette at each triage level. Each vignette was rendered as a realistic human-authored conversational query, and the AI chatbots were tasked with assigning a triage label from that disclosure. Emergency under-triage occurred in 23 of 410 level D trials (5.6%), and all under-triaged emergencies were reassigned to level C urgency. Across target models, average accuracy ranged from 42.0% to 71.8%. Accuracy was highest for level D vignettes (94.3%) and lowest for level B vignettes (19.7%). Mean signed ordinal error was positive (+0.47 triage levels), indicating net over-triage. Dispersion was highest around the middle triage levels. All results were confirmed relative to clinician consensus labels from 50 medical doctors. When presented with user messages containing sufficient clinical information, frontier AI chatbots thus recognized psychiatric emergencies as requiring urgent medical assessment with near-zero error rates, yet showed marked over-triage for low and intermediate risk presentations.

q-bio.NC↗

arXiv2026

A clinically validated framework for auditing AI chatbot behavior in mental health interactions

Millions of users turn to consumer AI chatbots to discuss emotional, behavioral, and mental-health concerns, creating an urgent need for rigorous and scalable safety evaluations. Here we introduce SIM-VAIL, a clinically validated framework for auditing chatbot behavior in mental-health contexts. SIM-VAIL simulates users with specific psychiatric vulnerabilities and conversational intents, engages them in multi-turn conversations with frontier AI chatbots (including Claude, ChatGPT, Gemini, Grok and Llama models), and scores each exchange across 13 clinically grounded risk dimensions. Across 810 conversations, spanning 9 target chatbots and 30 simulated user profiles, concerning behavior in target chatbots was widespread, albeit reduced in newer models. Concerning behavior varied by user vulnerability and conversational intent, accumulated over turns, and could be reduced by interventions at early escalation points. Risk was highest when otherwise supportive chatbot behaviors reinforced the psychological mechanisms underlying the simulated user's vulnerability, a pattern we term a Vulnerability Amplifying Interaction Loop (VAIL). SIM-VAIL provides a scalable framework for mapping mental health risk across users, chatbots, and conversational trajectories, offering a foundation for targeted safety improvements.

q-bio.NC↗

arXiv2025

Human-computer interactions predict mental health

Scalable assessments of mental illness remain a critical roadblock toward accessible and equitable care. Here, we show that everyday human-computer interactions encode high-dimensional information about self-reported psychological distress and wellbeing. We introduce MAILA, a MAchine-learning framework for Inferring Latent mental states from digital Activity. We trained MAILA on 18,200 cursor and touchscreen recordings labeled with 1.3 million mental-health self-reports collected from 9,500 participants. MAILA predicts dynamic mental states along 13 dimensions of distress and wellbeing, detects within-person changes over time, and resolves experimentally induced fluctuations in experienced arousal and valence. At the group level, MAILA recovers demographic and time-of-day patterns in self-reported mental health with high fidelity. MAILA also captures information only partially reflected in verbal self-report and, in a synthetic proof of concept, improves the ability of a frontier large language model to infer user mental health. By extracting signatures of psychological function that have so far remained untapped, MAILA provides the first large-scale, systematic proof of principle that human-computer interactions encode multidimensional and transferable information about mental health.

q-bio.NC↗