AI-Enhanced Predictive Maintenance of Medical Equipment
Abstract
The reliability and availability of medical equipment are critical for ensuring high-quality patient care. This paper investigates the use of artificial intelligence (AI) in predictive maintenance of medical devices. By analyzing sensor data, usage patterns, and historical maintenance records, AI algorithms can predict potential equipment failures and optimize maintenance schedules. The study highlights the benefits of AI-driven predictive maintenance, such as reduced downtime, lower maintenance costs, and improved equipment lifespan. Several case studies of hospitals and healthcare facilities implementing AI-enhanced maintenance strategies are presented. Challenges related to data integration, accuracy of predictions, and the adoption of AI technologies in healthcare infrastructure are also discussed.
References
Allamanis, M., Barr, E. T., Bird, C., & Sutton, C. (2015). Suggesting Accurate Method and Class Names. Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, 38-49.
Kulbir Singh, "MRI Brain Tumor Segmentation using Cuckoo Optimization and Ensemble CNNs", International Journal of Science and Research (IJSR), Volume 13 Issue 6, June 2024, pp. 425-434, https://www.ijsr.net/getabstract.php?paperid=SR24605090738
Iyer, S., Konstas, I., Cheung, A., & Zettlemoyer, L. (2016). Summarizing Source Code using a Neural Attention Model. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2073-2083.
McMillan, C., Grechanik, M., Poshyvanyk, D., Fu, C., & Xie, Q. (2011). Exemplar: A Source Code Search Engine with Natural Language Queries. Proceedings of the 2011 International Conference on Software Engineering, 832-835.
Allamanis, M., Tarlow, D., Gordon, A., & Wei, Y. (2015). Bimodal Modelling of Source Code and Natural Language. Proceedings of the 32nd International Conference on Machine Learning (ICML), 2123-2132.
Movshovitz-Attias, D., & Cohen, W. W. (2013). Natural Language Models for Predicting Programming Comments. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 35-40.
Sumit Mittal, "Framework for Optimized Sales and Inventory Control: A Comprehensive Approach for Intelligent Order Management Application," International Journal of Computer Trends and Technology, vol. 72, no. 3, pp. 61-65, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I3P109
Sridhara, G., Hill, E., Muppaneni, D., Pollock, L., & Vijay-Shanker, K. (2010). Towards Automatically Generating Summary Comments for Java Methods. Proceedings of the IEEE/ACM International Conference on Automated Software Engineering, 43-52.
Haiduc, S., Aponte, J., Moreno, L., & Marcus, A. (2010). On the Use of Automated Text Summarization Techniques for Summarizing Source Code. Proceedings of the 17th Working Conference on Reverse Engineering, 35-44.
Rodeghero, P., McMillan, C., McBurney, P. W., Bosch, N., & D’Mello, S. (2014). Improving Automated Source Code Summarization via an Eye-tracking Study of Programmers. Proceedings of the 36th International Conference on Software Engineering, 390-401.
Neha Dhaliwal. (2019), AUTOMATING ANALYSIS WORKFLOWS WITH AI: TOOLS FOR STREAMLINED DATA UPLOAD AND REVIEW IN CLINICAL SYSTEMS. (2019). JOURNAL OF BASIC SCIENCE AND ENGINEERING, 16(1), . https://yjgkx.org.cn/index.php/jbse/article/view/155
Moreno, L., Aponte, J., Marcus, A., & Pollock, L. (2013). Automatic Generation of Natural Language Summaries for Java Classes. Proceedings of the 21st International Conference on Program Comprehension (ICPC), 23-32.
Priyanka Koushik. (2024). Balancing Act: Optimization and Sustainability in B2B2C Supply Chain. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 3804–3813. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6149
Sridhara, G., Pollock, L., & Vijay-Shanker, K. (2011). Automatically Detecting and Describing High Level Actions within Methods. Proceedings of the 33rd International Conference on Software Engineering, 101-110.
Pansara, R. R. (2024). Master Data Quality and Business Rules: A Comprehensive Analysis. Saudi J Eng Technol, 9(2), 34-43.
Pansara, R. R. (2023). Master Data Management important for maintaining data accuracy, completeness & consistency. Authorea Preprints.
Pansara, R. R. (2023). Importance of Master Data Management in Agtech & Manufacturing Industry. Authorea Preprints.
Pansara, R. (2023). Digital Disruption in Transforming AgTech Business Models for a Sustainable Future. Transactions on Latest Trends in IoT, 6(6), 67-76.
