mlsanityData to 7 Oct 2026

All Required, In Order: Phase-Level Evaluation for AI-Human Dialogue in Healthcare and Beyond

arXiv 2601.08690 ↗First posted 13 Jan 2026cs.AIcited by 1

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Abstract (arXiv)

Conversational AI is starting to support real clinical work, but most evaluation methods miss how compliance depends on the full course of a conversation. We introduce Obligatory-Information Phase Structured Compliance Evaluation (OIP-SCE), an evaluation method that checks whether every required clinical obligation is met, in the right order, with clear evidence for clinicians to review. This makes complex rules practical and auditable, helping close the gap between technical progress and what healthcare actually needs. We demonstrate the method in two case studies (respiratory history, benefits verification) and show how phase-level evidence turns policy into shared, actionable steps. By giving clinicians control over what to check and engineers a clear specification to implement, OIP-SCE provides a single, auditable evaluation surface that aligns AI capability with clinical workflow and supports routine, safe use.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 24 of its 66 references that are in the map. First posted on arXiv 13 Jan 2026.

Builds on

24 of 66 references.

References · 24

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  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  3. 2024Evaluating Very Long-Term Conversational Memory of LLM Agentscited by 800
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12 more
  1. 2020STAR: A Schema-Guided Dialog Dataset for Transfer Learningcited by 43
  2. 2024GoldCoin: Grounding Large Language Models in Privacy Laws via Contextual Integrity Theorycited by 35
  3. 2024SHIELD: Evaluation and Defense Strategies for Copyright Compliance in LLM Text Generationcited by 33
  4. 2023NormDial: A Comparable Bilingual Synthetic Dialog Dataset for Modeling Social Norm Adherence and Violationcited by 24
  5. 2024MediTOD: An English Dialogue Dataset for Medical History Taking with Comprehensive Annotationscited by 21
  6. 2022Measuring Annotator Agreement Generally across Complex Structured, Multi-object, and Free-text Annotation Taskscited by 16
  7. 2022A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slotscited by 13
  8. 2022FineD-Eval: Fine-grained Automatic Dialogue-Level Evaluationcited by 13
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  10. 2023Towards Self-Adaptive Machine Learning-Enabled Systems Through QoS-Aware Model Switchingcited by 7
  11. 2024SWITCH: An Exemplar for Evaluating Self-Adaptive ML-Enabled Systemscited by 6
  12. 2024EcoMLS: A Self-Adaptation Approach for Architecting Green ML-Enabled Systemscited by 3

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