The study demonstrates that multi‑turn reinforcement learning (RL) in simulation environments significantly improves a language model’s performance on structured, text‑based psychiatric intake tasks. In a randomized, double‑blind crossover trial involving 60 matched scenario pairs, the RL‑trained agent outperformed its base model in 76.7% of cases, showing higher scores on clinical examination, patient‑centered communication, and differential diagnosis appropriateness. Across 127 validated simulated scenarios, the trained agent nearly doubled the base model’s clinical rubric score and reduced under‑triage of acute safety risks by more than half, indicating that simulation provides a controlled testbed for training and stress‑testing psychiatric conversational agents.
arXiv:2608. 07418v1 Announce Type: new Abstract: In medical education, physicians convert academic knowledge into clinical expertise through residency: years of training across thousands of encounters, with diverse sources of feedback and progressively greater autonomy.
arXiv:2607. 08257v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong performance on isolated psychiatric tasks, including dialogue, diagnosis, and treatment planning, yet existing benchmarks rarely simulate complete psychiatric clinical encounters.
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.
arXiv:2603.18008v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used for mental-health support; yet prevailing evaluation methods--fluency metrics, preference...
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.
arXiv:2609.15855v1 Announce Type: cross Abstract: % !TEX root = ../main.tex People increasingly use large language models (LLMs) for mental health support, yet their safety in evolving, high-risk con...
The study introduces a scaffolding-oriented multi-agent Large Language Model (LLM) AI Standardized Patient (AI-SP) platform designed to train medical students in patient interviews. In a randomized controlled trial with 100 students, the multi-agent system—comprising a patient agent, a Socratic tutor agent, and a turn-level evaluator—did not improve diagnostic accuracy but significantly enhanced overall OSCE scores, especially in communication, empathy, and history-taking. The authors also release a richly annotated dataset to support further research in AI-supported clinical reasoning training.
The paper introduces ClinMPO, an evidence‑guided reinforcement‑learning framework that enhances psychiatric reasoning in small language models (SLMs). ClinMPO leverages a reward model (ClinRM) trained on 18,569 question–answer pairs from 4,474 psychiatry articles and is guided by the psychiatrist‑defined Clinical Psychiatry Thinking Strategy (CPTS). Evaluations on four Qwen3 model sizes show that ClinMPO outperforms baseline, supervised fine‑tuning, and standard policy optimization, with the 8B model surpassing a human baseline of senior pre‑licensure medical students and achieving the highest rank among 31 models. Blinded clinician assessment confirms improved rationale quality across CPTS criteria, demonstrating that clinical evidence and specialist knowledge can be effectively incorporated into medical AI development.
arXiv:2610.02885v1 Announce Type: new Abstract: Mental health disorders affect a substantial proportion of the global population, yet a persistent shortage of trained practitioners leaves the majorit...
arXiv:2607. 25667v1 Announce Type: cross Abstract: Psychotherapists need repeated training and supervision by experts; however, scalability is problematic.
arXiv:2607. 13036v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for decision support in healthcare, but clinical evidence is often incomplete or evolving.
arXiv:2603.03677v2 Announce Type: replace-cross Abstract: Psychiatric consultation requires agents to elicit discriminative evidence, map uncertain narratives to diagnostic criteria, and decide when...