Authors : Poonam; Dr. V. K. Srivastva

Volume/Issue : Volume 11 - 2026, Issue 1 - January

Google Scholar : https://tinyurl.com/3kepv36s

Scribd : https://tinyurl.com/42cv6kyy

DOI : https://doi.org/10.38124/ijisrt/26jan1256

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This Artificial Intelligence (AI) and the Internet of Things (IoT) are changing the field of healthcare by introducing efficient, effective, and precise diagnostics on a real-time basis. The proposed research is an image classification system using deep learning to classify and detect kidney stones in stone surgery healthcare data, based on the convolutional neural networks (CNNs). The system uses edge computing to provide low-latency inference on IoT systems and uses explainable AI (XAI) techniques to provide model interpretability in order to give clinical trust. The proposed model has high accuracy, precision, recall, and F1-score that is proven through extensive experiments and is better than traditional machine learning solutions. The paper also covers the data security, privacy, and interoperability issues and gives a holistic framework of the AI-IoT-based smart healthcare systems. The results show the promise of AI-IoT combination to enhance the efficiency, patient outcomes, and workflow in clinical practice.

Keywords : Artificial Intelligence (AI), Internet of Things (IoT), Stone Surgery, Medical Image Classification, Deep Learning, Convolutional Neural Networks (CNN), Edge Computing, Explainable AI (XAI), Healthcare Informatics, Real-Time Diagnostics.

References :

1. Mizna, S., Arora, S., Saluja, P., Das, G., &Alanesi, W. A. (2025). An analytic research and review of the literature on practice of artificial intelligence in healthcare. European Journal of Medical Research, 30(1), 382.
2. Kumar, S., & Singh, D. (2025). 7 AI in Diagnostics and. Bioinformatics and Beyond: AI Applications in Healthcare, 130.
3. Bhardwaj, S., Gupta, G., Ahmad, S., & Haque, M. A. (2025). Transforming Healthcare With AI and IoT: A Path Towards to Efficiency and Sustainability. In Next-Generation Therapeutics Using Internet of Things and Machine Learning (pp. 1-26). IGI Global Scientific Publishing.
4. Deivayanai, V. C., Swaminaathanan, P., Vickram, A. S., Saravanan, A., Bibi, S., Aggarwal, N., ... & Abdel-Daim, M. M. (2025). Transforming healthcare: the impact of artificial intelligence on diagnostics, pharmaceuticals, and ethical considerations–a comprehensive review. International Journal of Surgery, 111(7), 4666-4693.
5. Baygin, M. (2025). Artificial Intelligence Technologies in Healthcare. In Business Challenges and Opportunities in the Era of Industry 5.0 (pp. 55-71). Emerald Publishing Limited.
6. Shang, Z., Chauhan, V., Devi, K., & Patil, S. (2024). Artificial intelligence, the digital surgeon: unravelling its emerging footprint in healthcare–the narrative review. Journal of Multidisciplinary Healthcare, 4011-4022.
7. Altunhan, A., Soyturk, S., Guldibi, F., Tozsin, A., Aydın, A., Aydın, A., ... & Ahmed, K. (2024). Artificial intelligence in urolithiasis: a systematic review of utilization and effectiveness. World Journal of Urology, 42(1), 579.
8. Punitha, S., & Preetha, K. S. (2024). Unleashing potential: a deep dive into AI-blockchain integration for UAV-enhanced tele-surgery. Cogent Engineering, 11(1).
9. Khan, K., Zameer, F., Jain, P., KR, R., Niranjan, V., S, M., &Padyana, S. (2024). Artificial intelligence in revolutionizing kidney care and beyond: Kid-AI revolution. Journal of Bio-X Research, 7, 0022.
10. Stitini, O., Ouakasse, F., Rakrak, S., Kaloun, S., &Bencharef, O. (2024). Combining IoMT and XAI for Enhanced Triage Optimization: An MQTT Broker Approach with Contextual Recommendations for Improved Patient Priority Management in Healthcare. International Journal of Online & Biomedical Engineering, 20(7).
11. AbdulMoniem, M., Abbas, T., Khandakar, A., Shuzan, M. N. I., Chowdhury, M. H., & Chowdhury, M. E. (2023). Artificial intelligence in hypospadiology: role, applications, and benefits. In Hypospadiology: Current Challenges and Future Perspectives (pp. 137-165). Singapore: Springer Nature Singapore.
12. Ali Mohamad, T., Bastone, A., Bernhard, F., & Schiavone, F. (2023). How artificial intelligence impacts the competitive position of healthcare organizations. Journal of Organizational Change Management, 36(8), 49-70.
13. Walia, G. K., Kumar, M., & Gill, S. S. (2023). AI-empowered fog/edge resource management for IoT applications: A comprehensive review, research challenges, and future perspectives. IEEE Communications Surveys & Tutorials, 26(1), 619-669.
14. Singh, J., Sillerud, B., & Singh, A. (2023). Artificial intelligence, chatbots and ChatGPT in healthcare—narrative review of historical evolution, current application, and change management approach to increase adoption. Journal of Medical Artificial Intelligence, 6.
15. Dou, F., Ye, J., Yuan, G., Lu, Q., Niu, W., Sun, H., ... & Song, W. (2023). Towards artificial general intelligence (agi) in the internet of things (iot): Opportunities and challenges. arXiv preprint arXiv:2309.07438.
16. Mullachery, B., &Alismail, S. (2022, October). A smart healthcare framework: opportunities for integrating emerging technologies (5G, IoT, AI, and GIS). In Proceedings of the Future Technologies Conference (pp. 325-340). Cham: Springer International Publishing.
17. Choudhury, A. (2022). Toward an ecologically valid conceptual framework for the use of artificial intelligence in clinical settings: need for systems thinking, accountability, decision-making, trust, and patient safety considerations in safeguarding the technology and clinicians. JMIR Human Factors, 9(2), e35421.
18. Comito, C., Falcone, D., &Forestiero, A. (2022). Convergence between iot and ai for smart health and predictive medicine. In Integrating Artificial Intelligence and IoT for Advanced Health Informatics: AI in the Healthcare Sector (pp. 69-84). Cham: Springer International Publishing.
19. Taj, I., & Zaman, N. (2022). Towards industrial revolution 5.0 and explainable artificial intelligence: Challenges and opportunities. International Journal of Computing and Digital Systems, 12(1), 295-320.
20. Paleti, S. (2022). The Role of Artificial Intelligence in Strengthening Risk Compliance and Driving Financial Innovation in Banking. Available at SSRN 5250770.
21. Wang, K., Zhao, Y., Gangadhari, R. K., & Li, Z. (2021). Analyzing the adoption challenges of the Internet of things (Iot) and artificial intelligence (ai) for smart cities in china. Sustainability, 13(19), 10983.
22. P. Sharma, N. R. Moparthi, S. Namasudra, V. Shanmuganathan, and C. Hsu, “Blockchain‐based <scp>IoT</scp> architecture to secure healthcare system using identity‐based encryption,” Expert Systems, vol. 39, no. 10. Wiley, Dec. 13, 2021. doi: 10.1111/exsy.12915
23. B. Zaabar, O. Cheikhrouhou, F. Jamil, M. Ammi, and M. Abid, “HealthBlock: A secure blockchain-based healthcare data management system,” Computer Networks, vol. 200. Elsevier BV, p. 108500, Dec. 2021. doi: 10.1016/j.comnet.2021.108500.
24. Y. Cui et al., “Automatic Detection and Scoring of Kidney Stones on Noncontrast CT Images Using S.T.O.N.E. Nephrolithometry: Combined Deep Learning and Thresholding Methods,” Mol. Imaging Biol., vol. 23, no. 3, pp. 436–445, 2021, doi: 10.1007/s11307-020-01554-0.
25. R. Mishr, A. Bhattacharjee, M. Gayathri, and C. Malathy, “Kidney stone detection with ct images using neural network,” Int. J. Psychosoc. Rehabil., no. May, 2020, doi: 10.37200/IJPR/V24I8/PR280269.