TY - GEN
T1 - Leveraging Large Language Models for Smart Pharmacy Systems
T2 - 19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025
AU - Osheba, Abdelrahman
AU - Abou-El-Ela, Ahmed
AU - Adel, Osama
AU - Maaod, Youssef
AU - Abuhmed, Tamer
AU - El-Sappagh, Shaker
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Adverse drug reactions (ADRs) remain a crucial challenge in healthcare systems, highly contributing to patient mortality. We present an innovative smart pharmacy system that utilizes advanced large language models (LLMs) to enhance drug safety and pharmacy operational efficiency. Our system integrates real-time data from patient's prescriptions, medication databases, and electronic health records (EHR) to automate the detection of potential drug interactions, optimizing clinical decision-making and reducing ADR-related risks. The system's architecture is designed to easily manage prescription processing for patients, inventory management and control, and Pharmacist consultations through an intelligent chatbot interface. Key features include real-time tracking of medication expiration and inventory levels, an interaction checker API for identifying and mitigating risky drug combinations, and an LLM-powered chatbot for accurate data analysis and visualization. By combining advanced computational techniques with AI-driven insights, our smart pharmacy system not only improves medication safety but also eases pharmacy operations. This transformative approach holds the potential to significantly reduce ADR-related hospital admissions and enhance overall healthcare delivery. Our research underscores the vital role of AI and LLMs in modern pharmacy practice, offering an inclusive solution that integrates easily into existing healthcare infrastructures. The proposed smart pharmacy system can be plugged into hospital EHR systems to automatically track patient medications.
AB - Adverse drug reactions (ADRs) remain a crucial challenge in healthcare systems, highly contributing to patient mortality. We present an innovative smart pharmacy system that utilizes advanced large language models (LLMs) to enhance drug safety and pharmacy operational efficiency. Our system integrates real-time data from patient's prescriptions, medication databases, and electronic health records (EHR) to automate the detection of potential drug interactions, optimizing clinical decision-making and reducing ADR-related risks. The system's architecture is designed to easily manage prescription processing for patients, inventory management and control, and Pharmacist consultations through an intelligent chatbot interface. Key features include real-time tracking of medication expiration and inventory levels, an interaction checker API for identifying and mitigating risky drug combinations, and an LLM-powered chatbot for accurate data analysis and visualization. By combining advanced computational techniques with AI-driven insights, our smart pharmacy system not only improves medication safety but also eases pharmacy operations. This transformative approach holds the potential to significantly reduce ADR-related hospital admissions and enhance overall healthcare delivery. Our research underscores the vital role of AI and LLMs in modern pharmacy practice, offering an inclusive solution that integrates easily into existing healthcare infrastructures. The proposed smart pharmacy system can be plugged into hospital EHR systems to automatically track patient medications.
KW - AI-powered chatbot
KW - Adverse drug reactions
KW - artificial intelligence
KW - clinical decision support
KW - drug safety
KW - electronic health records
KW - large language models
KW - patient safety
KW - smart pharmacy systems
UR - https://www.scopus.com/pages/publications/85218098116
U2 - 10.1109/IMCOM64595.2025.10857582
DO - 10.1109/IMCOM64595.2025.10857582
M3 - Conference contribution
AN - SCOPUS:85218098116
T3 - Proceedings of the 2025 19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025
BT - Proceedings of the 2025 19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025
A2 - Lee, Sukhan
A2 - Choo, Hyunseung
A2 - Ismail, Roslan
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 3 January 2025 through 5 January 2025
ER -