2025
doi: 10.12720/jait.16.10.1364-1378
An AI-Powered Public Health Kiosk for Context-Aware Over-the-Counter Medication Guidance: An Experimental Pilot Study
Sonya Falahati1,2
,
Morteza Alizadeh3 ,
Fatemeh Ghazipour1,4 ,
Zhino Safahi5 ,
Navid Khaledian6 ,
Mohammad R. Salmanpour1,7,*
- 1Technological Virtual Collaboration (TECVICO Corp.), Vancouver, Canada
- 2Electrical and Computer Engineering Department, Nooshirvani University of Technology, Babol, Iran
- 3Department of Mathematics, University of Isfahan, Isfahan, Iran
- 4Pharmacy Department, CDI College, Burnaby, Canada
- 5Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran
- 6Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- 7Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, Canada
- Manuscript receivedApril 8, 2025
- revisedMay 23, 2025
- acceptedJuly 14, 2025
- publishedOctober 14, 2025
Abstract
AI-powered public health kiosks have the potential to transform healthcare delivery by providing personalized Over-The-Counter (OTC) medication guidance. This study focuses on implementing a context-aware kiosk that offers tailored recommendations based on user-reported symptoms, allergies, and medication history, without the need for full clinical records. The system aims to enhance accessibility and provide practical self-care advice in public settings. The AI-powered kiosk integrates patient-specific factors such as allergies, age, and Drug-Drug Interactions (DDIs) to offer personalized OTC medication guidance. Built on the enhanced GAMENet architecture, incorporating Graph Attention Networks (GAT) and Multi-Head Cross-Attention (MHCA), the system surpasses traditional symptom checkers. The system was trained using the MIMIC-III dataset (6350 patients) and DDI data from the TWOSIDES database, with external validation performed on the MIMIC-IV dataset (9036 patients). Both datasets were divided into training (75%), validation (8.3%), and testing (16.7%) subsets. Real-time safety checks used Anatomical Therapeutic Chemical (ATC) codes. The kiosk interface includes multilingual support, large fonts, voice commands, and Braille. Usability and acceptability were assessed through a survey of 33 healthcare professionals. Preliminary results from the MIMIC-III dataset show that the enhanced GAMENet model achieved a Precision-Recall Area Under the Curve (PRAUC) of approximately 0.74. Validation with the MIMIC-IV dataset further demonstrated the model’s improved performance, highlighting its ability to deliver accurate and secure healthcare recommendations. This study demonstrates the potential of AI-powered kiosks to provide safe and personalized OTC medication guidance. Future work will focus on deployment, usability studies, and scalability for broader adoption.
Keywords
- public health kiosk
- AI and federated learning
- personalized healthcare
- Over-The-Counter (OTC) medication recommendation
- graph neural networks
How to Cite
Sonya Falahati, Morteza Alizadeh, Fatemeh Ghazipour, Zhino Safahi, Navid Khaledian, and Mohammad R. Salmanpour, "An AI-Powered Public Health Kiosk for Context-Aware Over-the-Counter Medication Guidance: An Experimental Pilot Study," Journal of Advances in Information Technology, vol. 16, no. 10, pp. 1364-1378, 2025. https://doi.org/10.12720/jait.16.10.1364-1378
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
