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The proliferation of Large Language Models and Intelligent Virtual Agents acting as psychotherapists offer expanded mental healthcare access.However, their deployment has led to serious harms due to a lack of methods to evaluate nuanced therapeutic risks.Current evaluation techniques lack the sensitivity to detect subtle changes in patient cognition and behavior during therapy sessions that may lead to subsequent decompensation.We introduce a risk ontology for the evaluation of conversational AI psychotherapists.Developed via literature review, expert interviews, and alignment with clinical criteria (DSM-5) and assessment tools (NEQ, UE-ATR), the ontology provides a structured approach to assessing patient harm.
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Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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