arXiv:2607.08257v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong performance on isolated psychiatric tasks, including dialogue, diagnosis, and treatment planning, yet existing benchmarks rarely simulate complete psychiatric clinical encounters. We introduce $\textbf{MentalHospital}$, a virtual evaluation environment for LLM-based psychiatric clinical encounters. MentalHospital instantiates the Subjective Interviewing, Objective Examination, Diagnostic Assessment, and Treatment Planning (S.O.A.P.) workflow, using skill-augmented standardized patients constructed fr
The proliferation of advanced LLMs necessitates more sophisticated and comprehensive evaluation environments that better simulate real-world, multi-step tasks, particularly in sensitive domains like healthcare.
This development allows for more accurate and robust assessment of AI capabilities in complex human interaction scenarios, paving the way for safer and more effective AI integration into critical sectors.
The standard for benchmarking LLMs in clinical settings moves beyond isolated tasks to full-E2E patient encounters, fostering more holistic and integrated AI solutions.
- · AI developers
- · Mental health patients (from improved AI tools)
- · Healthcare technology providers
- · Developers of narrow, task-specific AI benchmarks
- · Traditional diagnostic software
New benchmarks like MentalHospital will accelerate the development of more capable and reliable LLMs for healthcare.
Improved AI performance in clinical encounters could lead to the automation of certain diagnostic or therapeutic steps, increasing access to care.
The successful integration of AI into complex medical dialogues may set precedents for AI in other high-stakes human interaction roles, reshaping professional services.
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Read at arXiv cs.AI