Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64082
Volume:
10 |
5 |
May 2026
Pages:
1-13
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Artificial Intelligence and Data Science
Guru Gobind Singh College of Engineering And Research Centre
Artificial Intelligence and Data Science
Guru Gobind Singh College of Engineering And Research Centre
Artificial Intelligence and Data Science
Guru Gobind Singh College of Engineering And Research Centre
Artificial Intelligence and Data Science
Guru Gobind Singh College of Engineering And Research Centre
Artificial Intelligence and Data Science
Guru Gobind Singh College of Engineering And Research Centre
Today, digital technology is changing the healthcare field and making services easier to access. However, most healthcare systems are still not connected properly, and users have to use different apps for doctor consultation, booking appointments, finding medicines, and emergency help. This creates confusion and wastes time. This paper presents MediMate, a smart healthcare application that brings all important medical services into one platform. The system allows users to talk to doctors through video calls, book appointments easily, find nearby pharmacies, and request ambulance services during emergencies. It also includes an AI-based chatbot that helps users understand their symptoms in a simple and clear way by explaining the suggestions it gives. Another useful feature of the system is offline storage of health records, so users can access their prescriptions even without internet. The application is designed using modern technologies to make it reliable and easy to use. Testing results show that the system improves response time, reduces the need to switch between multiple apps, and makes healthcare services more convenient. MediMate aims to make healthcare simple, fast, and accessible for everyone by combining all services into one easy-to-use platform.
KEYWORDS
This work presents the design and development of a healthcare application called MediMate, which uses modern technologies to improve the accessibility and efficiency of medical services. The application brings together multiple important healthcare features into one platform, reducing the need for users to depend on different applications. It includes video consultation, appointment booking, digital prescription management, chatbot support, emergency ambulance request, and location-based pharmacy search. These features help users to easily connect with doctors, manage their health records, and access medical services without delay. For doctors, the system provides options to manage their profiles, set availability, handle appointments, and share prescriptions with patients. Overall, MediMate aims to reduce the gap between patients and healthcare providers by creating a simple and connected healthcare system. The application introduces several improvements compared to existing systems in different areas such as: • Smart Interaction: The chatbot uses explainable AI to provide health suggestions along with simple explanations, helping users understand the reason behind the advice. This improves user trust and makes the system more interactive. • Appointment Handling: Users can easily book appointments based on doctor availability, which reduces waiting time and improves communication between doctors and patients. • Integrated Services: By combining features like pharmacy locator, emergency services, and digital prescriptions, the system provides a complete healthcare solution in one place. There are still some areas where the system can be improved in the future to make it more effective: • Personalization: The current system provides general health suggestions, but future improvements can include more personalized recommendations based on user history and preferences. • Scalability: As the number of users increases, the system can be improved using cloud technologies to handle large data and provide better performance. • Security and Privacy: Stronger data protection methods can be added to ensure that user information remains safe and secure at all times. • Accessibility: Adding support for multiple languages and user-friendly features can help elderly and differently-abled users use the application more easily. • Healthcare Integration: Connecting the system with hospitals and verified healthcare providers can improve trust and accuracy of the services. Although the application is not yet deployed for public use, it has been tested using sample data to check its performance and working. The testing included checking features like chatbot responses, appointment booking, prescription management, and location-based services. The system showed good performance and was able to handle user requests effectively. Some minor issues like slower response during high usage and delays in map loading were observed, which can be improved in future updates. By addressing these limitations and adding more advanced features, MediMate can become a complete and reliable healthcare platform. It has the potential to improve healthcare access, reduce time, and provide better support to users in managing their health. In addition, MediMate has the potential to support future advancements in digital healthcare by integrating more advanced technologies such as real-time health monitoring and smart wearable device connectivity. This can help users track their health conditions continuously and receive timely alerts when needed. The system can also be improved by adding advanced AI models for better prediction and decision support, which can assist both users and doctors in making informed choices. By continuously improving its features and expanding its services, MediMate can become a powerful healthcare solution that not only simplifies medical processes but also improves the overall quality of healthcare services for users. Furthermore, MediMate can be enhanced by incorporating electronic health record (EHR) integration, allowing users’ medical history, test reports, and treatment records to be securely stored and accessed in a centralized system. This would enable doctors to make more accurate and faster clinical decisions by having a complete view of patient health data. Additionally, the platform can include AI-based predictive analytics to identify potential health risks at an early stage based on symptoms and historical data trends. Such capabilities can support preventive healthcare by alerting users before conditions become severe. Integration with insurance services can also simplify claim processing and reduce administrative burden for patients. By expanding in these directions, MediMate can evolve into a more intelligent, data-driven healthcare ecosystem that not only supports treatment but also emphasizes prevention and continuous care management. In addition to its current capabilities, MediMate represents a step toward a more connected and efficient healthcare ecosystem by bringing together patients, doctors, and healthcare services on a single platform. The system improves communication, reduces manual effort, and streamlines medical workflows, making healthcare access faster and more convenient. By simplifying complex processes such as consultations, prescriptions, and emergency services, it supports better decision-making and improves the overall user experience. With its unified approach, MediMate demonstrates how digital technology can transform traditional healthcare delivery into a more organized, user-friendly, and responsive system. MediMate provides a unified platform that simplifies healthcare access by integrating multiple services such as consultations, appointments, and medical assistance in one place. This helps reduce delays and improves coordination between patients and healthcare providers.
[1] S.-K. Tan, S.-C. Chong, K.-K. Wee, and L.-Y. Chong, “Personalized healthcare: A comprehensive approach for symptom diagnosis and hospital recommendations using AI and location services,” Journal of Informatics and Web Engineering, vol. 3, no. 1, pp. 117–135, Feb. 2024. Available: https://journals.mmupress.com/index.php/jiwe/article/view/795 [2] M. Abinaya and R. Ganesan, “Effective search mechanism for finding nearest healthcare facilities,” in Proc. Global Conf. Communication Technology (GCCT), 2015, pp. 534–538. Available: https://ieeexplore.ieee.org/abstract/document/7342719 [3] M. Wasim Munir, S. Muhammad Omair, and M. Zeeshan Ul Haque, “An Android based application for determining a specialized hospital nearest to patient’s location,” International Journal of Computer Applications, vol. 118, no. 9, pp. 43–46, 2015. Available: https://www.ijcaonline.org/archives/volume118/number9/20776-3316/ [4] R. A. Nimbalkar and R. A. Fadnavis, “Domain specific search of nearest hospital and healthcare management system,” in Proc. Recent Advances in Engineering and Computer Science (RAECS), 2014, pp. 1–5. Available: https://ieeexplore.ieee.org/abstract/document/6799536 [5] A. Kumar, “Disease prediction and doctor recommendation system using machine learning approaches,” International Journal of Research in Applied Science and Engineering Technology, vol. 9, no. 7, pp. 34–44, 2021. [6] L. Björkdahl, O. Pauli, J. Östman, C. Ceccobello, S. Lundell, and M. Kjellberg, “Towards holistic disease risk prediction using small language models,” 2024, arXiv:2408.06943. [7] R. Umbare, R. Patil, T. Mukund, A. Rathod, and P. Rajurkar, “A smart healthcare application using artificial intelligence (AI) and machine learning (ML),” in Proc. 5th Int. Conf. Image Process. Capsule Netw. (ICIPCN), 2024, pp. 204–210.Available: https://ieeexplore.ieee.org/abstract/document/10660966 [8] D. Gujar, R. Biyani, T. Bramhane, S. Bhosale, and T. P. Vaidya, “Disease prediction and doctor recommendation system,” International Research Journal of Engineering and Technology, vol. 5, pp. 3207–3209, 2018. [9] D. Mogaveera, V. Mathur, and S. Waghela, “E-health monitoring system with diet and fitness recommendation using machine learning,” in Proc. 6th Int. Conf. Inventive Comput. Technologies (ICICT), 2021, pp. 694–700.Available: https://ieeexplore.ieee.org/abstract/document/9358605 [10] P. Tanwar, K. Bansal, A. Sharma, and N. Dagar, “AI based chatbot for healthcare using machine learning,” in Proc. Int. Conf. Advanced Computing, Communication and Information Technology, 2023, pp. 751–755. Available: https://ieeexplore.ieee.org/abstract/document/10465728
Publication Date
5/26/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
5
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...