AI model struggles to infer patient state from clinical transcripts

A new research paper explores the limits of inferring patient state and conversational structure from clinical encounter transcripts. Using a GPT-5 deployment for annotation and validation on 439 transcripts, the study found that while conversational phase structure is observable and useful, patient state is only partially observable. This suggests caution against relying solely on transcripts for inferring human state, even in clinical settings designed to elicit detailed patient information. AI

IMPACT Highlights limitations of LLMs in inferring nuanced human states from text, impacting applications in healthcare and beyond.

RANK_REASON Academic paper detailing a research methodology and findings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• Conversation as Measurement in Clinical Encounters: Observable Phase Structure, Partially Observable Patient State
• GPT-5
• Hugging Face

• paper
• safety

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