Advancing Medical IT Ecosystems Through AI-Driven Automation and Human Expertise: Bridging the Gap Between Technology and Clinical Practice
Keywords:
Artificial Intelligence, Medical IT Ecosystems, Clinical Automation, Human Expertise, Healthcare Technology IntegrationAbstract
The integration of Artificial Intelligence (AI) within medical IT ecosystems offers transformative potential for enhancing clinical workflows, decision-making, and operational efficiency. Despite advancements, the need to balance AI-driven automation with human expertise remains critical to address ethical, practical, and reliability challenges. This paper explores current innovations, their limitations, and pathways to synergize technology with clinical practice for improved healthcare outcomes.
References
Smith, J., & Taylor, M. (2021). "AI in Diagnostics: Reducing Errors through Automation." Journal of Clinical Technology, 28(4), 345-359.
Johnson, R., & Lee, A. (2020). "Optimizing Healthcare Operations with AI." Health Informatics Journal, 26(2), 201-217.
Nivedhaa N. (2024). Explainable AI (XAI) in Healthcare: Interpretable Models for Clinical Decision Support. International Journal of Computer Science and Information Technology Research, 5(2), 33-40.
Valaboju, P.K. (2024). The Synergistic Impact of Human-AI Collaboration: A Multi-Domain Analysis. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(5), 934–942. https://doi.org/10.32628/cseit241051083
Brown, H., et al. (2022). "Collaborative AI Models in Clinical Decision Making." Nature Medicine, 29(7), 634-645.
Topol, E. J. (2019). "High-performance medicine: the convergence of human and artificial intelligence." Nature Medicine, 25(1), 44–56.
Valaboju, P.K. (2024). Transformative Impact of AI and Cloud Technologies: A Comparative Analysis across Healthcare, Retail, and Mobile Financial Services. IJFMR, 6(6), November-December 2024. https://doi.org/10.36948/ijfmr.2024.v06i06.29894
Esteva, A., Robicquet, A., Ramsundar, B., et al. (2019). "A guide to deep learning in healthcare." Nature Medicine, 25(1), 24–29.
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). "AI in health and medicine." Nature Medicine, 28(1), 31–38.
Valaboju, P.K. (2024). Integration of AI and Cloud Technologies in Healthcare: A Comprehensive Framework for Career Development and Portfolio Enhancement. International Journal of Research in Computer Applications and Information Technology, 7(2), 674–687. https://doi.org/10.5281/zenodo.14034269
Hamet, P., & Tremblay, J. (2017). "Artificial intelligence in medicine." Metabolism: Clinical and Experimental, 69, S36–S40.
Lee, J., McCullough, J., & Weller, E. (2020). "AI ethics in healthcare: Navigating data privacy, bias, and accountability." Health Informatics Journal, 26(4), 255–272.
Valaboju, P.K. (2024). Integrating Cloud Technologies for Enhanced Healthcare Systems: A Comprehensive Approach. International Journal of Computer Engineering and Technology, 15(5), 1093–1101. https://doi.org/10.5281/zenodo.14035251
Liu, X., Faes, L., Kale, A. U., et al. (2019). "A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis." The Lancet Digital Health, 1(6), e271–e297.
Obermeyer, Z., & Emanuel, E. J. (2016). "Predicting the future—big data, machine learning, and clinical medicine." The New England Journal of Medicine, 375(13), 1216–1219.
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Copyright (c) 2025 Ahmad Jefry Abd Hashim Md (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
