arXiv AI
MIND: Unified Inquiry and Diagnosis RL with Criteria Grounded Clinical Supports for Psychiatric Consultation
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arXiv AI
Jul 10
MentalHospital: A Virtual Environment for Evaluating Psychiatric Clinical Encounters
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.
arXiv AI
Jul 22
MedDDC-Eval: Diagnosis-Decoupled Evaluation of Multi-Turn Medical Consultation Agents
arXiv:2607. 18999v1 Announce Type: cross Abstract: Multi-turn medical consultation agents must decide what to ask, adapt to patient responses, and determine when the collected evidence is sufficient.
arXiv AI
Aug 14
AnchorSIPS: A Synthetic Dataset and Evaluation Resource for Evidence-Supported Psychosis-Risk Symptom Measurement
arXiv:2608. 12329v1 Announce Type: cross Abstract: Progress on AI for psychosis-risk assessment is limited by a data-access bottleneck.
arXiv AI
Jul 16
Ask Before You Diagnose: Safe-Psych, a Sequential Evaluation Benchmark for LLMs in Psychiatry
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 Computation and Language
3d ago
Evidence-Bounded Mental Health Reasoning from Heterogeneous Speech Protocols
The paper introduces Evidence-Bounded Mental Health Reasoning, addressing the problem that current multimodal mental health screening models treat all clinical speech protocols as equally evidential. It presents the Evidence Package Benchmark, comprising 1,870 annotated packages from six diverse protocols, and proposes EviBound, a protocol-aware framework that limits reasoning to valid evidence using a planner, acoustic consensus, and a boundary critic. EviBound outperforms existing omni-modal baselines, achieving a Depression AUROC of 0.8658 with no claim violations.
arXiv Computation and Language
3d ago
An evidence-guided reinforcement learning method to improve psychiatric reasoning in small language models
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.