# HealthDial: A No-Code LLM-Assisted Dialogue Authoring Tool for Healthcare Virtual Agents

arXiv: https://arxiv.org/abs/2510.15898 · HTML: https://mlsanity.com/paper/2510.15898/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 5 of its 39 references that are in the map.
- First posted on arXiv 10 Sep 2025.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 5 of 5)

- [Polaris: A Safety-focused LLM Constellation Architecture for Healthcare](https://mlsanity.com/paper/2403.13313/) (2024)
- [Virtual Agents for Alcohol Use Counseling: Exploring LLM-Powered Motivational Interviewing](https://mlsanity.com/paper/2407.08095/) (2024)
- [Scaffolding Empathy: Training Counselors with Simulated Patients and Utterance-level Performance Visualizations](https://mlsanity.com/paper/2502.18673/) (2025)
- [Synthetic Patients: Simulating Difficult Conversations with Multimodal Generative AI for Medical Education](https://mlsanity.com/paper/2405.19941/) (2024)
- [Investigating User Perceptions of Collaborative Agenda Setting in Virtual Health Counseling Session](https://mlsanity.com/paper/2407.06123/) (2024)

## Built on by (most cited first, 1 of 1)

- [Designing and Evaluating an AI-enhanced Immersive Multidisciplinary Simulation (AIMS) for Interprofessional Education](https://mlsanity.com/paper/2510.08891/) (2025)

## Abstract (arXiv)

We introduce HealthDial, a dialogue authoring tool that helps healthcare providers and educators create virtual agents that deliver health education and counseling to patients over multiple conversations. HealthDial leverages large language models (LLMs) to automatically create an initial session-based plan and conversations for each session using text-based patient health education materials as input. Authored dialogue is output in the form of finite state machines for virtual agent delivery so that all content can be validated and no unsafe advice is provided resulting from LLM hallucinations. LLM-drafted dialogue structure and language can be edited by the author in a no-code user interface to ensure validity and optimize clarity and impact. We conducted a feasibility and usability study with counselors and students to test our approach with an authoring task for cancer screening education. Participants used HealthDial and then tested their resulting dialogue by interacting with a 3D-animated virtual agent delivering the dialogue. Through participants' evaluations of the task experience and final dialogues, we show that HealthDial provides a promising first step for counselors to ensure full coverage of their health education materials, while creating understandable and actionable virtual agent dialogue with patients.