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Clinician-Grounded Quality Assurance for AI-Assisted Psychiatric Intake — arXiv:2609.21149

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.21149
License (as archived): CC0-1.0
Archived: 2026-10-10T01:01:43.003Z
SHA-256 of the archived text: 4cfd96a421751badbbdb6505c74dc938c48529413b086906bda93d284f6d3181
Stable URL of this page: https://metraipolis.com/documento/dc_feaf4e688366

How to cite

Clinician-Grounded Quality Assurance for AI-Assisted Psychiatric Intake — arXiv:2609.21149 — arXiv (en), https://arxiv.org/abs/2609.21149 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_feaf4e688366 (Metraipolis archive dc_feaf4e688366, sha256 4cfd96a421751badbbdb6505c74dc938c48529413b086906bda93d284f6d3181)

BibTeX

@misc{dcfeaf4e688366,
  title = {Clinician-Grounded Quality Assurance for AI-Assisted Psychiatric Intake — arXiv:2609.21149},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.21149},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_feaf4e688366; sha256 4cfd96a421751badbbdb6505c74dc938c48529413b086906bda93d284f6d3181},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.21149. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 InterviewPlayground with our expert-authored vignettes, constructed a simulated intake platform for the interviews, and designed evaluation modalities relevant to intake. In a pilot of 6 clinicians in a 25-minute assessment compared to a GPT-based LLM intake interviewer, the LLM recovered more of the clinically relevant items embedded in the patient vignettes (88.0% vs. 38.9%), but made more clinical inferences not based on the interview (56.8% vs. 27.8%), and characterized identified safety concerns less often (33.3% vs. 66.7%), setting the stage for deployed quality assurance for this task.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-21 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2609.21149 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-10T00:23:42.431Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-21?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).