AI-Powered Symptom Assessment and User Experience: A Case Study of Simtomi and Simtomi-Care
arXiv:2609.38187 · doi:10.1109/CAI68641.2026.11536388
Abstract
Digital symptom checkers are widely used for quick guidance on health concerns, yet many systems still face challenges in collecting accurate information, supporting communication, or integrating with clinical workflows. To explore how these tools function in real use, we examine the case of the Simtomi system, which pairs a multilingual symptom assessment application with a provider-facing platform. Empirical studies were conducted in two countries. In South Korea, based on participants' firsthand experience, we found that the system improved how patients communicated their symptoms and helped clinicians review cases more efficiently through structured summaries aligned with diagnostic reasoning. In the United States, responses from prospective users and healthcare professionals highlighted the value of multilingual support, structured questioning, and the system's potential to assist clinical coordination. These findings offer a grounded account of how AI-based symptom assessment tools can operate across different healthcare contexts and provide broader insight into usability, trust, and usefulness in digital health.
6 pages, 5 figures; published in the 2026 IEEE Conference on Artificial Intelligence (CAI)