Clinician-Grounded Quality Assurance for AI-Assisted Psychiatric Intake
arXiv cs.AIen
arXiv:2609.21149v1 Announce Type: new Abstract: Before patients can use AI-assisted psychiatric intake systems, health systems need practical ways to routinely evaluate these tools against their clinical standards for quality assurance. Because clinicians may use different intake styles, evaluation for this task must (1) support comparison across interviewing approaches, (2) minimize clinician burden, and (3) measure clinically relevant performance for health systems deploying these technologies. We present a clinician-grounded evaluation platform built around a memory-augmented patient simulator for open-ended AI interviewing, InterviewPlayground. We created interactive patients using Inter
This is a short summary published by AI Global Wire. The full article is owned and hosted by arXiv cs.AI — open it there to read it in full.
Read the full story at arXiv cs.AI- Verktyg
- Forskning
- Företag
Related AI news
- A researcher used GPT-6 Astra to decipher a WWI German radio transmission from 1918, one of the 50 famous unsolved ciphers listed on a German science blog (prinz)Techmeme · September 21, 2026
- Hong Kong-based Qupital, which offers cross-border ecommerce financing to SMEs, raised a $300M Series C led by M Capital as it weighs a possible IPO (FinTech Global)Techmeme · September 21, 2026
- China slows humanoid robot IPO rush as hype outruns realityEconomic Times Tech · September 21, 2026
- Styr AI-agenter som om de vore anställda – men låtsas inte att de är människorComputer Sweden · September 21, 2026
- DENSE: Distilling Agent Trajectories into Evidence-Grounded Shortcut Trees for Self-RefinementarXiv cs.AI · September 21, 2026
- GVPO++: Group Variance Policy Optimization for LLM Post-Training and On-Policy DistillationarXiv cs.AI · September 21, 2026