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State-Aware Virtual Patient Simulation for Training Psychiatrists in Challenging Clinical Scenarios — arXiv:2611.02067

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.02067
License (as archived): CC0-1.0
Archived: 2026-11-03T10:22:28.109Z
SHA-256 of the archived text: f4fe777af117682f23d6eb772e145c2a468bd48d90c850a52eda90ecf3007430
Stable URL of this page: https://metraipolis.com/documento/dc_ea855aa663e7

How to cite

State-Aware Virtual Patient Simulation for Training Psychiatrists in Challenging Clinical Scenarios — arXiv:2611.02067 — arXiv (en), https://arxiv.org/abs/2611.02067 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ea855aa663e7 (Metraipolis archive dc_ea855aa663e7, sha256 f4fe777af117682f23d6eb772e145c2a468bd48d90c850a52eda90ecf3007430)

BibTeX

@misc{dcea855aa663e7,
  title = {State-Aware Virtual Patient Simulation for Training Psychiatrists in Challenging Clinical Scenarios — arXiv:2611.02067},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.02067},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ea855aa663e7; sha256 f4fe777af117682f23d6eb772e145c2a468bd48d90c850a52eda90ecf3007430},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.02067. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Psychiatrists must be prepared to manage patients exhibiting challenging psychological states, including suicidal ideation, hallucinations, and other high-risk behaviors. However, opportunities to safely practice such interactions are limited. While recent conversational agents can simulate patients, existing approaches largely rely on large language models (LLMs) to determine patient responses, making it difficult to consistently maintain psychological state, adapt to the clinician's responses, and generate clinically plausible challenges that meaningfully test the clinician. We present a state-aware virtual patient simulation pipeline for training psychiatrists in challenging clinical scenarios. The proposed system maintains an evolving patient state throughout the interaction and orchestrates patient behavior rather than leaving response generation entirely to an LLM. At each interaction, a learning-based behavioral strategy selector determines the type and level of challenge appropriate to the current patient and clinician context. The selected strategy, patient state, clinician response, interaction history, and relevant clinical knowledge are then provided as grounded context to an LLM to generate a semantically relevant and psychologically consistent patient response. Following each session, the system analyzes the interaction and provides feedback to the clinician on their communication and management of the virtual patient.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.02067 — 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 2026-11-03T10:19:49.849Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).