Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64534
Volume:
10 |
6 |
Jun 2026
Pages:
1-8
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.
Department of computer engineering
Terna Engineering College, Nerul
Department of computer engineering
Terna Engineering College, Nerul
Department of computer engineering
Terna Engineering College, Nerul
The rapid advancement of digital healthcare technologies has significantly transformed the way healthcare services are delivered, monitored, and accessed. With the growing demand for accessible and intelligent healthcare solutions, there is a need for systems that can provide preliminary health guidance and personalized recommendations. This paper presents HealthSyncIQ, an AI-based digital health guidance system designed to assist users in symptom analysis, health monitoring, and healthcare decision-making. The platform collects user information such as symptoms, age, medical history, and health-related parameters to analyze potential health conditions and generate appropriate recommendations. Artificial Intelligence techniques are employed to process user inputs and provide preliminary healthcare guidance, including precautions, medication information, and health recommendations. In addition, the system integrates various healthcare services such as an AI assistant, medicine search, doctor finder, fitness tracking, weekly health reports, notifications, and feedback management within a unified platform. The proposed solution aims to improve healthcare accessibility, reduce response time, and enhance user engagement through intelligent and personalized healthcare support. By combining multiple healthcare functionalities into a single ecosystem, HealthSyncIQ provides an efficient, scalable, and user-friendly approach to digital healthcare. The system contributes toward the development of smart healthcare solutions and supports users in making informed health-related decisions while promoting better health management practices.
KEYWORDS
The proposed HealthSyncIQ platform presents an intelligent and integrated digital healthcare solution designed to provide symptom analysis, personalized healthcare recommendations, and health monitoring services. The system combines various healthcare functionalities such as AI assistance, symptom analysis, medicine search, doctor finder, fitness tracking, weekly health reports, notifications, and administrative management within a single platform. The implementation of intelligent healthcare technologies improves healthcare accessibility and supports users in obtaining preliminary healthcare guidance in an efficient manner. The developed system provides a user-friendly environment that helps users monitor health-related information and make informed decisions regarding their well-being. The results indicate that HealthSyncIQ can enhance user experience, reduce response time, and improve the effectiveness of healthcare assistance by integrating multiple healthcare services into a unified system. The modular architecture of the platform also provides scalability and flexibility for future development. In the future, additional features such as real-time wearable device integration, advanced disease prediction models, telemedicine support, multilingual communication, and enhanced AI capabilities can be incorporated to further improve the functionality and effectiveness of the system. HealthSyncIQ can contribute toward the development of smart and accessible digital healthcare ecosystems for future healthcare applications.
[1] R. Jiang, S. H. Nguyen, and Y. Li, “Artificial intelligence in healthcare: Applications and future directions,” Journal of Healthcare Engineering, vol. 2022, pp. 1–12, March 2022. [2] A. Gupta and P. Sharma, “Machine learning techniques for disease prediction using healthcare data,” International Journal of Medical Informatics, vol. 165, pp. 104–118, June 2022. [3] S. Verma and R. Singh, “AI-based healthcare monitoring and disease prediction systems,” IEEE Access, vol. 11, pp. 23140–23158, February 2023. [4] M. Patel, K. Shah, and J. Desai, “Symptom-based disease prediction using machine learning algorithms,” Procedia Computer Science, vol. 218, pp. 124–133, 2023. [5] H. Lee and J. Kim, “Digital healthcare systems and wearable technologies for patient monitoring,” Journal of Biomedical Informatics, vol. 138, pp. 104287, April 2023. [6] S. Wang, P. Zhang, and Y. Chen, “Healthcare recommendation systems using artificial intelligence techniques,” Expert Systems with Applications, vol. 223, pp. 119924, January 2024. [7] R. Kumar and A. Mishra, “Intelligent symptom analysis and disease prediction using deep learning,” IEEE Transactions on Artificial Intelligence, vol. 5, no. 2, pp. 312–325, March 2024. [8] T. Brown and K. Wilson, “AI-driven digital health technologies for personalized healthcare,” Journal of Medical Systems, vol. 48, no. 5, pp. 1–15, May 2024. [9] P. Joshi and V. Patil, “Smart healthcare systems using machine learning and cloud computing,” International Journal of Computer Applications, vol. 186, no. 17, pp. 22–30, July 2024. [10] Y. Zhang and L. Chen, “Artificial intelligence applications in healthcare diagnostics,” Healthcare Technology Letters, vol. 12, no. 1, pp. 10–18, January 2025.
Publication Date
6/5/2026
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...