SIMAX: A Scalable and Interpretable Framework for Multi-Fidelity and Annotated Clinician-Patient Dialogue Simulation
arXiv:2606.30491v1 Announce Type: cross Abstract: Background. The widespread deployment of ambient digital scribes is driving large-scale capture of clinician-patient dialogues. Human coding of clinical communication data remains costly, inconsistent, and difficult to scale, motivating AI-driven communication coding systems. However, evaluating these systems requires real-world dialogues and human-coded labels, both hard to obtain at scale. Methods. We developed SIMAX (Scalable and Interpretable Framework for Multi-Fidelity and Annotated Clinician-Patient Dialogue Simulation), a framework for
The rapid deployment of ambient digital scribes is generating vast quantities of clinician-patient dialogue data, creating an urgent need for scalable and reliable AI-driven communication coding systems.
This framework addresses the significant challenge of evaluating and improving AI systems in sensitive medical contexts by providing a method to simulate realistic dialogues and annotations at scale, reducing reliance on costly human coding.
The ability to simulate multi-fidelity, annotated clinician-patient dialogues allows for more efficient development and testing of AI communication coding, potentially accelerating the deployment of reliable medical AI tools.
- · AI healthcare developers
- · Healthcare providers
- · Patients
- · Medical AI research
- · Manual data annotation services
- · Traditional clinical trial methodologies
More robust and rapidly deployable AI communication tools emerge for healthcare.
Reduced operational costs in healthcare through more efficient clinical documentation and improved diagnostic support.
Accelerated medical research and personalized treatment plans due to higher quality and quantity of analyzed clinical interaction data.
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Read at arXiv cs.AI