SIMAX generates 3,388 controlled simulated clinician-patient dialogues with behavioral annotations across specialties and evaluates their quality and utility for communication coding systems.
Lukac, William Turner, Sitaram Vangala, Aaron T
3 Pith papers cite this work, alongside 48 external citations. Polarity classification is still indexing.
3
Pith papers citing it
48
external citations · OpenAlex
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Single-agent LLM frameworks outperform naive multi-agent systems in multimodal clinical risk prediction tasks and are better calibrated.
A multi-channel governance framework for a deployed ambient AI scribe achieved measurable improvements in clinician-validated performance and feedback quality through continuous rubric evaluation, live monitoring, and controlled experiments.
citing papers explorer
-
SIMAX: A Scalable and Interpretable Framework for Multi-Fidelity and Annotated Clinician-Patient Dialogue Simulation
SIMAX generates 3,388 controlled simulated clinician-patient dialogues with behavioral annotations across specialties and evaluates their quality and utility for communication coding systems.
-
AgentRx: A Benchmark Study of LLM Agents for Multimodal Clinical Prediction Tasks
Single-agent LLM frameworks outperform naive multi-agent systems in multimodal clinical risk prediction tasks and are better calibrated.
-
End-to-End Evaluation and Governance of an EHR-Embedded AI Agent for Clinicians
A multi-channel governance framework for a deployed ambient AI scribe achieved measurable improvements in clinician-validated performance and feedback quality through continuous rubric evaluation, live monitoring, and controlled experiments.