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LLM-guided policy outperforms other methods in psychiatric intake question selection

Researchers have developed a new benchmark and an LLM-guided adaptive policy for question selection in conversational psychiatric intake. The system aims to improve information recovery by intelligently choosing questions from a large bank based on patient responses. In evaluations, the LLM-guided approach outperformed both random questioning and a fixed clinical form, particularly in challenging patient scenarios. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT This research introduces a benchmark for evaluating conversational AI in clinical settings, potentially improving diagnostic accuracy and efficiency.

RANK_REASON Academic paper introducing a new benchmark and methodology for AI in clinical settings.

Read on arXiv cs.CL →

COVERAGE [2]

  1. arXiv cs.CL TIER_1 · Guan Gui, Peter Zandi, Jacob Taylor, Ananya Joshi ·

    Optimal Question Selection from a Large Question Bank for Clinical Field Recovery in Conversational Psychiatric Intake

    arXiv:2604.22067v1 Announce Type: new Abstract: Psychiatric intake is a sequential, high-stakes information-gathering process in which clinicians must decide what to ask, in what order, and how to interpret incomplete or ambiguous responses under limited time. Despite growing int…

  2. arXiv cs.CL TIER_1 · Ananya Joshi ·

    Optimal Question Selection from a Large Question Bank for Clinical Field Recovery in Conversational Psychiatric Intake

    Psychiatric intake is a sequential, high-stakes information-gathering process in which clinicians must decide what to ask, in what order, and how to interpret incomplete or ambiguous responses under limited time. Despite growing interest in conversational AI for healthcare, there…