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arXiv · 2504.13880

An AI-powered Public Health Automated Kiosk System for Personalized Care: An Experimental Pilot Study

Sonya Falahati·Morteza Alizadeh·Fatemeh Ghazipour·Zhino Safahi·Navid Khaledian·Mohammad R. Salmanpour
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Abstract

Background: The HERMES Kiosk (Healthcare Enhanced Recommendations through Artificial Intelligence & Expertise System) is designed to provide personalized Over-the-Counter (OTC) medication recommendations, addressing the limitations of traditional health kiosks. It integrates an advanced GAMENet model enhanced with Graph Attention Networks (GAT) and Multi-Head Cross-Attention (MHCA) while ensuring user privacy through federated learning. This paper outlines the conceptual design and architecture of HERMES, with a focus on deployment in high-traffic public areas. Methods: HERMES analyzes self-reported symptoms and anonymized medical histories using AI algorithms to generate context-aware OTC medication recommendations. The system was initially trained using Electronic Health Records (EHR) from the MIMIC-III dataset (6,350 patients) and Drug-Drug Interaction (DDI) data from the TWOSIDES database, incorporating the top 90 severity DDI types. Real-time DDI checks and ATC-mapped drug codes further improve safety. The kiosk is designed for accessibility, offering multilingual support, large fonts, voice commands, and Braille compatibility. A built-in health education library promotes preventive care and health literacy. A survey was conducted among 10 medical professionals to evaluate its potential applications in medicine. Results: Preliminary results show that the enhanced GAMENet model achieved a Precision-Recall AUC (PRAUC) of 0.74, outperforming the original model. These findings suggest a strong potential for delivering accurate and secure healthcare recommendations in public settings. Conclusion: HERMES demonstrates how AI-driven, privacy-preserving kiosks can enhance public health access, empower users, and alleviate burdens on healthcare systems. Future work will focus on real-world deployment, usability testing, and scalability for broader adoption.

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Sonya Falahati, Morteza Alizadeh, Fatemeh Ghazipour, Zhino Safahi, Navid Khaledian, Mohammad R. Salmanpour. 2025-06-02. An AI-powered Public Health Automated Kiosk System for Personalized Care: An Experimental Pilot Study. https://arxiv.org/abs/2504.13880

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