THE IMPACT OF ARTIFICIAL INTELLIGENCE ON HEALTHCARE CURRENT APPLICATIONS AND FUTURE PROSPECTS | IJCT Volume 12 – Issue 6 | IJCT-V12I6P72
International Journal of Computer Techniques
ISSN 2394-2231
Author
Susheel Kumar, Vipin Rawat, Km Divya
Abstract
The healthcare industry has been undergoing a significant transformation with the integration of Artificial Intelligence (AI) technologies. AI encompasses a variety of techniques, including machine learning (ML), natural language processing (NLP), robotics, and predictive analytics, each offering unique potential for enhancing healthcare services. This paper examines the impact of AI on healthcare by analyzing current applications, challenges, and potential future developments. The research question guiding this study is: How has AI been applied in healthcare, what challenges have emerged, and what future prospects exist for its adoption and integration? To explore this, a combination of qualitative and quantitative research methods was employed. Data were collected from a survey of healthcare professionals to understand their perceptions of AI’s role and challenges. Additionally, case studies from hospitals and clinics that have adopted AI technologies were reviewed to analyze real-world applications. Results indicate that AI has made notable contributions to diagnostic accuracy, personalized treatments, and administrative efficiency, but there are ongoing challenges in data security, ethical considerations, and resistance to technological change. The paper concludes with recommendations for overcoming these barriers and explores the future trajectory of AI in areas such as personalized medicine, robotic surgery, and telemedicine.
Keywords
Artificial Intelligence (AI), Healthcare technology, Machine learning (ML), Predictive analytics, Diagnostic tools, Medical imaging, Natural language processing (NLP), Robotics in healthcare, Telemedicine, Personalized medicine, Data privacy,
Conclusion
This research highlights the transformative potential of AI in healthcare, from improving diagnostic accuracy to streamlining administrative tasks and enhancing patient care. While AI has made significant strides in healthcare, challenges related to data privacy, ethical concerns, and integration with existing healthcare infrastructures remain. Future research should focus on developing AI systems that are secure, transparent, and free from bias to ensure equitable healthcare delivery. Additionally, AI’s role in personalized medicine, robotic surgery, and telemedicine will continue to evolve, offering exciting opportunities for improving healthcare outcomes and expanding access to care.
References
[1]. Esteva, A., Kuprel, B., Novoa, R. A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.
[2]. Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. The New England Journal of Medicine, 380(14), 1347-
1358.
[3]. Shin, D. (2018). Data privacy and the future of AI in healthcare. Journal of Healthcare Privacy and Security, 24(3), 45-60.
[4]. Obermeyer, Z., Powers, B. W., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453.
[5]. Topol, E. J. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.
[6]. Jiang, F., Jiang, Y., Zhi, H., et al. (2017). Artificial intelligence in healthcare: Past, present and future. Seminars in Cancer Biology, 18(1), 1-12. [7]. Esteva, A., Kuprel, B., Novoa, R. A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.
[8]. Topol, E. J. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.
[9]. Obermeyer, Z., Powers, B. W., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453.
[10]. Chen, M., Ma, Y., Li, Y., et al. (2017).
Wearable devices for health monitoring: A review of recent developments. IEEE Access, 5, 12260-12279.
[11]. Rajpurkar, P., Irvin, J., Zhu, K., et al. (2018). Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to radiologists. PLOS Medicine, 15(11), e1002686.
[12]. Liu, Y., Chen, P. C., Krause, J., & Peng, L. (2019). How to read articles that use machine learning: Users’ guides to the medical literature. JAMA, 322(18), 1806-
1816.
How to Cite This Paper
Susheel Kumar, Vipin Rawat, Km Divya (2025). THE IMPACT OF ARTIFICIAL INTELLIGENCE ON HEALTHCARE CURRENT APPLICATIONS AND FUTURE PROSPECTS. International Journal of Computer Techniques, 12(6). ISSN: 2394-2231.
