arXiv (All)
open access
When Patients Cut In: Extending Clinical Conversational AI Safety to Interruptions
Clinical voice agents are now deployed in routine care, where real patients do not wait their turn: they interrupt. These systems typically use a cascaded architecture (speech-to-text -> LLM -> text-to-speech), so when a patient cuts the agent off mid-utterance, clinically required content can be lo
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- When Patients Cut In: Extending Clinical Conversational AI Safety to Interruptions#790033
- Transforming Patient Voices into Early Predictors of Survival Using Nonlinear Mixed-Effect Models and AI/ML for Patient-Centered Decision-Making#243527
- Machine Learning Assisted Recognition of Out-of-Hospital Cardiac Arrest During Emergency Calls.#871867
- Applying Deep Learning Models on Perioperative Electronic Health Records for the Prediction of Postoperative Delirium#408422
- Generative AI at the Bedside: An Integrative Review of Applications and Implications in Clinical Nursing Practice.#785677
Record · ID 794052
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive