arXiv:2607. 08625v1 Announce Type: new Abstract: Consumer-facing health chatbots powered by large language models (LLMs) are increasingly used for symptom assessment.
By Jo\~ao Matos, Olivia Buege, Donny Cheung, Gary S. Collins, Paula Dhiman, Nan Li, Bingyu Mao, Benjamin W. Nelson, Michail Ouroutzoglou, Paul Varghese, Jonathan Amar
arXiv:2604.25415v2 Announce Type: replace-cross
Abstract: People increasingly turn to general-purpose AI chatbots for advice about emotional and mental health problems, but the ability of these syste...
By Veith Weilnhammer, Lennart Luettgau, Christopher Summerfield, Raymond Dolan, Elise Wilkinson, Virginia Corno, Viknesh Sounderajah, Matthew M Nour
arXiv:2606. 30491v1 Announce Type: cross Abstract: Background.
By Zhuhan Bao, Rui Yang, Bohao Yang, Zhiyi Liu, Sicheng Shu, Ruio Heerschap, Le Li, Doris Yang, Elisabeth Bond, Haoyuan Wang, Nicoleta Economou-Zavlanos, Joshua M. Biro, Matthew McDermott, Nan Liu, Anand Chowdhury, Kai Sun, Kathryn Pollak, Ed Hammond, Chuan Hong
The paper evaluates the safety of conversational AI therapy bots for Generation Alpha, revealing that while these models understand 76‑82% of youth‑specific vocabulary, they correctly assess clinical risk only 64‑72% of the time, creating a significant vocabulary‑comprehension gap. Six failure patterns—such as sarcasm masking, minimization acceptance, and semantic drift—were identified, with compounded errors leading to a 94% miss rate when three or more patterns co‑occur. The authors estimate 146,880 missed crises annually and recommend mandatory human‑in‑the‑loop systems, quarterly youth‑specific validation, transparent performance disclosure, and regulatory oversight for youth‑facing mental health AI.
By Manisha Mehta, Virendra Mehta
MindEval2 is a new evaluation framework for AI chatbots in mental health support, comprising Mental Health Quality Rubrics (MHQR) and the MindSim patient simulation harness. MHQR, developed with PhD-level psychologists, provides explicit, actionable criteria for assessing response quality, while MindSim offers realistic, longitudinal patient interactions with a memory component. The study evaluated 18 models, finding that although surface-level quality has improved, guidance calibration remains the main failure across all models.
By John Mendon\c{c}a, Daniel Tschernutter, Jos\'e Pombal, Areti Vassilopoulos, Catarina Botelho, Ignacio Sarasua, Maya D'Eon, Nuno Guerreiro, Ricardo Rei
arXiv:2602. 05088v4 Announce Type: replace Abstract: Millions of people now use generative AI chatbots for psychological support.
By Kate H. Bentley, Luca Belli, Adam M. Chekroud, Emily J. Ward, Emily R. Dworkin, Emily Van Ark, Kelly M. Johnston, Will Alexander, Millard Brown, Matt Hawrilenko