The paper introduces HealthCUES, a real‑time streaming pipeline that extracts and analyzes cough and throat‑clearing events from live spoken conversations. It detects coughs within sub‑second latency, distinguishes cough subtypes (dry, wet, barking, whooping), differentiates coughing from throat clearing, and estimates temporal boundaries, all while gating alerts based on conversational context. The system, built on Qwen3Omni, achieves high accuracy (93% F1 for cough detection) and low latency (340 ms) and has been validated by healthcare professionals for telehealth use.
By Tanmay Laud, Herprit Mahal, Subhabrata Mukherjee
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
arXiv:2607. 25485v1 Announce Type: new Abstract: Health AI is evolving from answering questions to agentic systems that converse with patients, reason about health records, and act on their behalf.
By Korosh Vatanparvar, Ashutosh Joshi, Maria Xenochristou, Mohammad Abuzar Hashemi, Prasad Kasu, Deepak Bansal, Daniel Lopez-Martinez, Anchal Nema, Ramya Ganesan, Will Kimbrough, Alex Woody, Yadunandana Rao, Dilek Hakkani-Tur, Wilko Schulz-Mahlendorf
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
Anian is a safety‑gated multimodal AI backend designed for perinatal mental‑health support and mindfulness‑intervention routing. It maps user input into a four‑layer hierarchical state representation—emotion, psychosocial constructs, safety risk, and intervention routes—then fuses local and external risk signals to decide whether to generate AI responses or provide fixed safety content. Prototype evaluation on large public corpora showed high classification performance and perfect high‑risk recall in a controlled stress test, though clinical validity remains unestablished.
By Lei Wang, Xiao Wang, Lei Li
arXiv:2608.29241v1 Announce Type: new
Abstract: Clinical voice agents are now deployed in routine care, where real patients do not wait their turn: they interrupt. These systems typically use a casca...
By Zachary Ellis, Spencer Hazel, Adam Brandt, Yajie Vera He, Ernest Lim, Jared Joselowitz