InterviewPlayground pilot shows LLM recovers 88% clinical items but underreports safety
Health systems that plan to deploy AI‑assisted psychiatric intake tools need a way to verify that those systems meet clinical quality standards. The authors propose a clinician‑grounded evaluation platform called InterviewPlayground, built around a memory‑augmented patient simulator that can generate open‑ended interview scenarios from expert‑authored vignettes.
Key points
- InterviewPlayground is a memory‑augmented patient simulator for open‑ended AI psychiatric interviews.
- In a pilot with six clinicians, the GPT‑based LLM recovered 88.0% of clinically relevant items versus 38.9% for clinicians.
- The LLM made more unfounded clinical inferences (56.8% vs 27.8%) and identified safety concerns less often (33.3% vs 66.7%).
In a pilot involving six clinicians who each performed a 25‑minute assessment using the simulator, the authors compared the clinicians’ performance to that of a GPT‑based large language model acting as an intake interviewer. The LLM extracted 88.0% of the clinically relevant items embedded in the vignettes, while clinicians captured 38.9%. However, the model also generated more clinical inferences that were not grounded in the interview (56.8% vs. 27.8%) and identified safety concerns less frequently (33.3% vs. 66.7%).
The results illustrate that while AI interviewers can retrieve a higher proportion of relevant clinical information, they may also miss or misinterpret safety signals, underscoring the need for systematic quality‑assurance processes before wide deployment.
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