arXiv AI August 20, 2026 By Shreeya Sharma, Ravish Gupta, Saket Kumar, Abhishek Aggarwal

Pedagogical AI in Mental Health: A Tri-Stream Fine-Tuned LLM Framework for Automated Clinical Supervision and Risk Triage

The paper introduces a fine‑tuned Mistral‑7B‑instruct model as an automated "Supervisor‑in‑the‑Loop" for mental health care, using a tri‑stream analysis of therapeutic alliance, latent risk, and supervisory triage. Leveraging 106 DAIC‑WOZ sessions, the VAL (Visual‑Acoustic‑Linguistic) framework achieves high accuracy in technique identification, alliance assessment, and risk prediction, while cutting supervisory triage latency from 72 hours to about 10 seconds per session. The system also addresses cold‑start challenges with Bayesian priors and synchronizes modalities via timestamps for robust fusion.

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arXiv AI

Jul 15

First, do NOHARM: a medical safety benchmark and randomized study of physician and AI teaming on clinical consultations

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llms rag agents benchmarks safety

arXiv AI

Jul 28

OpenAIs HealthBench in Action: Evaluating an LLM-Based Medical Assistant on Realistic Clinical Queries

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llms rag agents benchmarks

arXiv AI

Jul 10

MentalHospital: A Virtual Environment for Evaluating Psychiatric Clinical Encounters

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llms reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Aug 7

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By Ivan Lopez, Selin S. Everett, Bryan J. Bunning, April S. Liang, Dong Han Yao, Shivam C. Vedak, Kameron C. Black, Sophie Ostmeier, Stephen P. Ma, Emily Alsentzer, Jonathan H. Chen, Akshay S. Chaudhari, Eric Horvitz

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arXiv AI

Aug 7

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By Abdulkadir K\"ul\c{c}e, Alihan Esen, Ca\u{g}la Fikir, Berke Kurt, Kuzey Arar, G\"okhan Ercan, Faik Boray Tek

llms agents multimodal benchmarks safety

arXiv AI

Jun 11

Human-Guided Agentic AI for Multimodal Clinical Prediction: Lessons from the AgentDS Healthcare Benchmark

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By Lalitha Pranathi Pulavarthy, Raajitha Muthyala, Aravind V Kuruvikkattil, Zhenan Yin, Rashmita Kudamala, Saptarshi Purkayastha

agents multimodal benchmarks safety

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Related stories

First, do NOHARM: a medical safety benchmark and randomized study of physician and AI teaming on clinical consultations

OpenAIs HealthBench in Action: Evaluating an LLM-Based Medical Assistant on Realistic Clinical Queries

MentalHospital: A Virtual Environment for Evaluating Psychiatric Clinical Encounters

Clinician input steers AI toward accurate and harmful recommendations

ECHO: A Locally-Deployable Agentic Health Assistant with Temporal Memory, Safety Guardrails, and Speech Assessment

Human-Guided Agentic AI for Multimodal Clinical Prediction: Lessons from the AgentDS Healthcare Benchmark