AI-Powered Health Hub: A Hybrid Multi-Algorithm Approach for Smart Healthcare Automation

Abstract

The rapid advancements in artificial intelligence (AI) and large language models (LLMs) have opened new opportunities for transforming patient engagement in healthcare through conversational AI. This paper presents an AI-powered healthcare platform named (AP2H) AI-Powered Health Hub, designed to automate medical symptom analysis, prescription digitization, and medicine recommendation—specifically focusing on their applications in analyzing and generating conversations for improved patient engagement. The system combines machine learning ensembles, optical character recognition (OCR), and convolutional neural networks (CNN) to deliver common lan- guage and context-aware diagnostics. The hybrid multi-algorithm framework achieves an overall accuracy of 90% in symptom detection and disease prediction (details and evaluation method- ology are provided in Section VI). The proposed model aims to improve early diagnosis, streamline healthcare workflows, optimal solutions to patients problems and enhance accessibility through real-time intelligent health insights. The study proposes a hybrid approach based on algorithms as a successful solution for balancing and optimizing clinical decision support. Integrating AI into healthcare raises important ethical considerations regarding data privacy, bias, transparency, and regulatory compliance; we briefly discuss best practices for responsible deployment.

Introduction

1. Vuka, L. R. Salvador, and E. Kadena, “AI in Healthcare: Applications, Challenges and Opportunities,” in Proc. IEEE Int. Conf. Intelligent Engineering Systems (INES), 2024, pp. 230–233.
2. Anonymous, “Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review,” Journal/Conference Name, 2023.
3. T. Davenport and R. Kalakota, “The potential for artificial intelligence in healthcare,” Future Healthcare Journal, vol. 6, no. 2, pp. 94–98, 2019.
4. A. Esteva et al., “A guide to deep learning in healthcare,” Nature Medicine, vol. 25, no. 1, pp. 24–29, 2019.
5. F. Jiang et al., “Artificial intelligence in healthcare: past, present and future,” Stroke and Vascular Neurology, vol. 2, no. 4, pp. 230–243, 2017.
6. J. Bajwa et al., “Artificial Intelligence in healthcare: transforming the practice of medicine,” Future Healthcare Journal, vol. 8, no. 2, pp. e188–e193, 2021.
7. R. H. Koushal et al., “AI-Powered Symptom Checker Chatbot in Regional Languages,” in 2025 IEEE International Conference on In- terdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), 2025.
8. A. Sinhal et al., “Optimizing Diagnostic Accuracy in Healthcare by using Deep Learning,” in 4th IEEE World Conference on Applied Intelligence and Computing, 2025.
9. B. Wen et al., “Leveraging Large Language Models for Patient Engage- ment: The Power of Conversational AI in Digital Health,” in 2024 IEEE International Conference on Digital Health (ICDH), 2024.
10. V. Samokisheva et al., “Risks and Challenges of Using Ai In Health- care,” in 2024 12th International Scientific Conference on Computer Science (COMSCI), 2024.
11. A. K. Roy et al., “Multidisease Prediction and Causality Analysis with Hospital Management System,” in Proceedings of the 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC- 2024), 2024.
12. J. K. A. et al., “Virtual Health Assist: An LLM-Powered AI Platform for Symptom Diagnosis and Healthcare Assistance,” in 2025 Third International Conference on Networks, Multimedia and Information Technology (NMITCON), 2025.
13. D. Mendhe et al., “AI-Enabled Data-Driven Approaches for Personalized Medicine and Healthcare Analytics,” in Unknown Conference/Journal, 2025.
14. F. Zhu et al., “Design of AI in the Health and Elderly Care Service Platform in the Big Data Environment,” in 2023 IEEE International Conference on Paradigm Shift in Information Technologies with Inno- vative Applications in Global Scenario, 2023.
15. P. Chakraborty et al., “MediAI: AI-Driven Healthcare Revolution for Smarter Diagnostics and Treatment,” in 2025 International Conference on Engineering Innovations and Technologies (ICoEIT), 2025.
16. R. Verma et al., “Simplifying Medical Report: A Novel Approach to Medical Reporting using OCR Technology,” in 2025 3rd International Conference on Communication, Security, and Artificial Intelligence (ICCSAI), 2025.

Conclusion

The proposed AP2H (AI-Powered Health Hub) demonstrates the effectiveness of integrating machine learning en- sembles, OCR-based prescription digitization, CNN-driven medical image analysis, and conversational AI into a unified multimodal healthcare automation system. By processing text, prescription images, and diagnostic reports within a single framework, the platform delivers reliable, context-aware pre- dictions and supports rapid preliminary health assessment for users. The modularity and low-latency performance of the system highlight its potential for real-time digital healthcare applications. While the initial results are encouraging, several enhancements are planned for future development. These include large-scale benchmarking using clinically validated datasets to strengthen model robustness, the incorporation of explainable AI (XAI) methods to improve transparency and trustworthiness, and the addition of regional language support to im- prove accessibility for diverse user groups. Further extensions involve integrating IoT-based health monitoring devices for continuous data collection and real-time alerts. Long-term future work includes clinician-in-the-loop evaluation, pilot testing in healthcare environments, and ensuring regulatory compliance for safe and ethical deployment.

References

[1] Vuka, L. R. Salvador, and E. Kadena, “AI in Healthcare: Applications, Challenges and Opportunities,” in Proc. IEEE Int. Conf. Intelligent Engineering Systems (INES), 2024, pp. 230–233. [2] Anonymous, “Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review,” Journal/Conference Name, 2023. [3] T. Davenport and R. Kalakota, “The potential for artificial intelligence in healthcare,” Future Healthcare Journal, vol. 6, no. 2, pp. 94–98, 2019. [4] A. Esteva et al., “A guide to deep learning in healthcare,” Nature Medicine, vol. 25, no. 1, pp. 24–29, 2019. [5] F. Jiang et al., “Artificial intelligence in healthcare: past, present and future,” Stroke and Vascular Neurology, vol. 2, no. 4, pp. 230–243, 2017. [6] J. Bajwa et al., “Artificial Intelligence in healthcare: transforming the practice of medicine,” Future Healthcare Journal, vol. 8, no. 2, pp. e188–e193, 2021. [7] R. H. Koushal et al., “AI-Powered Symptom Checker Chatbot in Regional Languages,” in 2025 IEEE International Conference on In- terdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), 2025. [8] A. Sinhal et al., “Optimizing Diagnostic Accuracy in Healthcare by using Deep Learning,” in 4th IEEE World Conference on Applied Intelligence and Computing, 2025. [9] B. Wen et al., “Leveraging Large Language Models for Patient Engage- ment: The Power of Conversational AI in Digital Health,” in 2024 IEEE International Conference on Digital Health (ICDH), 2024. [10] V. Samokisheva et al., “Risks and Challenges of Using Ai In Health- care,” in 2024 12th International Scientific Conference on Computer Science (COMSCI), 2024. [11] A. K. Roy et al., “Multidisease Prediction and Causality Analysis with Hospital Management System,” in Proceedings of the 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC- 2024), 2024. [12] J. K. A. et al., “Virtual Health Assist: An LLM-Powered AI Platform for Symptom Diagnosis and Healthcare Assistance,” in 2025 Third International Conference on Networks, Multimedia and Information Technology (NMITCON), 2025. [13] D. Mendhe et al., “AI-Enabled Data-Driven Approaches for Personalized Medicine and Healthcare Analytics,” in Unknown Conference/Journal, 2025. [14] F. Zhu et al., “Design of AI in the Health and Elderly Care Service Platform in the Big Data Environment,” in 2023 IEEE International Conference on Paradigm Shift in Information Technologies with Inno- vative Applications in Global Scenario, 2023. [15] P. Chakraborty et al., “MediAI: AI-Driven Healthcare Revolution for Smarter Diagnostics and Treatment,” in 2025 International Conference on Engineering Innovations and Technologies (ICoEIT), 2025. [16] R. Verma et al., “Simplifying Medical Report: A Novel Approach to Medical Reporting using OCR Technology,” in 2025 3rd International Conference on Communication, Security, and Artificial Intelligence (ICCSAI), 2025.

Copyright

Copyright © 2026 Dr. Girija S, Darshan N. R., Gurukiran Y. G., Mukunda U., Sanjeev S. H.. 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.