arXiv AI By Jiwon Kim, Violeta J. Rodriguez, Dong Whi Yoo, Eshwar Chandrasekharan, Koustuv Saha

PAIR-SAFE: A Paired-Agent Approach for Runtime Auditing and Refining AI-Mediated Mental Health Support

Read the original on arXiv AI →

PAIR‑SAFE is a paired‑agent framework that pairs a Responder agent with a Judge agent grounded in the clinically validated Motivational Interviewing Treatment Integrity (MITI‑4) to audit and refine AI‑mediated mental health support in real time. The Judge evaluates each response and issues “allow” or “revise” decisions, guiding the Responder toward more clinically aligned interactions. Experiments with a support‑seeker simulator show that Judge‑supervised interactions significantly improve MITI dimensions such as Partnership, Seek Collaboration, and overall relational quality, with findings corroborated by expert evaluation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Jul 1

Training Therapeutic Judges and Multi-Agent Systems for Human-Aligned Mental Health Support

arXiv:2606. 30887v1 Announce Type: cross Abstract: Large language models show promise for mental health support, yet therapeutic quality improves only when evaluation functions as an actionable control signal rather than a passive metric.

By Mizanur Rahman, Abeer Badawi, Elahe Rahimi, Laleh Seyyed-Kalantari, Frank Rudzicz, Enamul Hoque, Elham Dolatabadi
arXiv AI
Sep 21

Clinician-Grounded Quality Assurance for AI-Assisted Psychiatric Intake

The paper introduces a clinician‑grounded evaluation platform called InterviewPlayground, which uses a memory‑augmented patient simulator to assess AI‑assisted psychiatric intake systems. It supports comparison across different interviewing styles, reduces clinician workload, and measures clinically relevant performance. In a pilot study, a GPT‑based intake interviewer captured more relevant items but made more unfounded inferences and missed safety concerns compared to clinicians.

By King Shi, Amanda Li, Jonathan Ivey, Synthia Qia Wang, Guan Gui, Hyunseo Kim, Peter Zandi, Jason Straub, Jacob Taylor, Ananya Joshi
arXiv AI
Jul 29

PatientAgentBench: A Benchmark Framework for Evaluating Patient-Facing Health AI Agents

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 AI
Oct 7

PsyCIDRA: A Dual-Agent Framework for Psychiatric Interviewing and Diagnostic Reasoning

PsyCIDRA is a dual‑agent framework that couples a free‑form psychiatric interviewer with a diagnostic reasoning agent to support expert review. The interviewer agent uses tools to keep working notes, load expert skills, and pull ICD‑11 references, while the diagnostic agent receives the interview transcript and generates hypotheses with supporting, conflicting, and missing evidence, withholding a final hypothesis if insufficient support exists. In simulations and a blinded human study, PsyCIDRA achieved higher diagnostic agreement and rank‑1 accuracy than direct prompting, indicating its promise for assisting psychiatric assessment through interactive dialogue.

By Milad Mohammadi, Fatemeh Akrami Shamsabadi, Zahra Mohseni, Amirhossein Safdarian, Malekfarhad Malek, Hadi Moradi, Hesham Faili