Narender Singh,
Tushar Latiyan,
Nitin Sharma,
Mohd. Shad,
- Student, Department of Computer Science and Engineering, Meerut Institute of Engineering and Technology, Meerut, Uttar Pradesh, India
- Student, Department of Computer Science and Engineering, Meerut Institute of Engineering and Technology, Meerut, Uttar Pradesh, India
- Student, Department of Computer Science and Engineering, Meerut Institute of Engineering and Technology, Meerut, Uttar Pradesh, India
- Student, Department of Computer Science and Engineering, Meerut Institute of Engineering and Technology, Meerut, Uttar Pradesh, India
Abstract
Medication plays a crucial role in today’s world, and with technological advancements, automation has become essential to save time and effort. One such innovation is “Medical Store Automation”, an automated system designed to enhance healthcare management. This system incorporates an automatic drawer-opening or cabinet system integrated with stock inventory. Its primary objective is to locate the desired medicine quickly by automatically opening the corresponding drawer, minimizing time spent searching. This study introduces a system named “Medicine Place Finder and Auto Inventory Management System”, which efficiently updates and manages medicine stock. The integration of advanced technologies such as AI-driven inventory tracking, digital prescription management, and automated billing has not only increased efficiency but also reduced operational costs. These systems allow medical stores to maintain accurate stock records, prevent medication shortages, and avoid overstocking, leading to better financial management. Additionally, automated reminders for prescription refills and real-time customer service support contribute to a seamless and personalized customer experience.
Keywords: HTML, CSS, JavaScript, PHP, artificial intelligence, auto inventory system
[This article belongs to Journal of Artificial Intelligence Research & Advances ]
Narender Singh, Tushar Latiyan, Nitin Sharma, Mohd. Shad. Automated Systems for Efficient Medical Stores. Journal of Artificial Intelligence Research & Advances. 2025; 12(02):13-20.
Narender Singh, Tushar Latiyan, Nitin Sharma, Mohd. Shad. Automated Systems for Efficient Medical Stores. Journal of Artificial Intelligence Research & Advances. 2025; 12(02):13-20. Available from: https://journals.stmjournals.com/joaira/article=2025/view=208151
References
- Khandagale SS, Bappasaheb NO, Khose AS, Kshirsagar P, Nemane P, Thube RH. Role of Pharmaceutical Automation and Robotics in Pharmaceutical Industry: A Review. Syst Rev Pharm. 2024 Mar 1; 15(3): 131–135.
- Fouad H. Embedded System Design of Remote Healthcare Monitoring Center Using Web-Technology. Int J Robot Mechatron. 2015; 2(2): 59–64.
- Çakıcı ÖE, Groenevelt H, Seidmann A. Using RFID for the management of pharmaceutical inventory—system optimization and shrinkage control. Decis Support Syst. 2011 Nov 1; 51(4): 842–52.
- Bhardwaj K, Alam R, Pandeya A, Sharma PK. Artificial Intelligence in Pharmacovigilance and COVID-19. Curr Drug Saf. 2023 Feb 1; 18(1): 5–14.
- Hussein BR, Kasem A, Omar S, Siau NZ. A data mining approach for inventory forecasting: A case study of a medical store. In Computational Intelligence in Information Systems: Proceedings of the Computational Intelligence in Information Systems Conference (CIIS 2018) 3. Cham: Springer International Publishing; 2019; 178–188.
- Lo’ai AT, Mehmood R, Benkhlifa E, Song H. Mobile cloud computing model and big data analysis for healthcare applications. IEEE Access. 2016 Sep 26; 4: 6171–80.
- Bhatnagar N. Role of robotic process automation in pharmaceutical industries. InThe International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019) 4. Cham: Springer International Publishing; 2020; 497–504.
- Patak M, Lostakova H, Curdova M, Vlckova V. The e-pharmacy customer segmentation based on the perceived importance of the retention support tools. Procedia-Soc Behav Sci. 2014 Sep 15; 150: 552–62.
- Karthiayani A, Raman R. IoT-Based Smart Pharmacies for Optimizing Stock Management with Long Short-Term Memory Model. In 2024 IEEE 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). 2024 Mar 14; 1–6.
- Zhang Z, Guo L, Si R, Chalmers L, Filippin P, Carpenter J, Czarniak P. Pharmacists’ perceptions on real-time prescription monitoring (RTPM) systems–a cross-sectional survey. Explor Res Clin Soc Pharm. 2022 Mar 1; 5: 100122.

Journal of Artificial Intelligence Research & Advances
| Volume | 12 |
| Issue | 02 |
| Received | 01/03/2025 |
| Accepted | 24/03/2025 |
| Published | 16/04/2025 |
| Publication Time | 46 Days |
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