The Role of AI in Modern Healthcare Systems

Year : 2025 | Volume : 03 | Issue : 02 | Page : 69 76
    By

    Jaspreet Kaur,

  • Sukhpreet Singh,

  1. Assistant Professor, Faculty of Computing, Guru Kashi University, Talwandi Sabo, Punjab, India
  2. Assistant Professor, Professor Faculty of Computing, Guru Kashi University, Talwandi Sabo, Punjab, India

Abstract

In the Indian pandemic, several issues in the healthcare system have brought to the forefront the imperative of hospitals shifting from manual medical records to computerized healthcare information systems. These solutions offer an effective method for integrating computer-based decision support tools and communicating e-healthcare information. With increasing dependence on AI-based solutions, a strong IT infrastructure is essential for improving healthcare quality, data security, and controlling increasing medical expenses. Advances in communication technology and networking are fundamental to the modernization of healthcare services and addressing both patients’ and providers’ needs. An AI-powered hospital system is not only necessary but also feasible despite its challenges. This paper discusses the uses of IT in hospitals and medical environments. They could improve clinical judgment, the recording of imaging and medical history, reduce duplication of diagnostic tests, and enhance reliability on medication with improved use of healthcare resources, and enhanced preventive health programs

Keywords: Artificial Intelligence in Healthcare, Clinical Decision Support Systems, Electronic Health Records (EHR), Healthcare IT Infrastructure, Medical Data Management

[This article belongs to International Journal of Bioinformatics and Computational Biology ]

How to cite this article:
Jaspreet Kaur, Sukhpreet Singh. The Role of AI in Modern Healthcare Systems. International Journal of Bioinformatics and Computational Biology. 2025; 03(02):69-76.
How to cite this URL:
Jaspreet Kaur, Sukhpreet Singh. The Role of AI in Modern Healthcare Systems. International Journal of Bioinformatics and Computational Biology. 2025; 03(02):69-76. Available from: https://journals.stmjournals.com/ijbcb/article=2025/view=236480


References

  1. Metev SM, Veiko VP. Laser Assisted Microtechnology. 2nd ed. Osgood RM Jr, editor. Berlin, Germany: Springer-Verlag; 1998.
  2. Gaikwad S. Mobile agents in heterogeneous database environment for emergency healthcare system. In: 5th International Conference on Information Technology New Generations (ITNG); 2008 Apr 7-9; Las Vegas, NV, USA. IEEE Computer Society; 2008. p. 1220–1. ISBN:978-0-7695-3099–4.
  3. Son Y, Kim H, Kim E, Choi S, Lee S. Application of support vector machine for prediction of medication adherence in heart failure patients. Healthc Inform Res. 2010;16(4):253.
  4. Kampouraki P, Belsis D, Vassis C, Skourlas C. e-Doctor: A web based support vector machine for automatic medical diagnosis. Procedia Soc Behav Sci. 2013;73:467–74.
  5. Mount L, Smith R, Douglas C, Kelman C. An integrated electronic health record and information system for Australia? Med J Aust. 2000;172:25–7.
  6. Augusstine NMJ, Samy SRN. Smart healthcare monitoring system using support vector machine. Aust J Sci Technol. 2018;2(3):1–8.
  7. Kaninde R, et al. Remote health monitoring system using noninvasive mobile technology. Int J Recent Innov Trends Comput Commun. 2016;4(11):196–9.
  8. Gaikwad S, et al. Comparative study of machine learning techniques in the medical field. Int J Comput Sci Mob Comput. 2020;9(12):59–67.
  9. Martín-Campillo A, Martínez-García C, Cucurull J, Martí R, Robles S, Borrell J. Mobile agents in healthcare, a distributed intelligence approach. In: Bichindaritz I, Vaidya S, Jain A, Jain LC, editors. Computational Intelligence in Healthcare 4. Studies in Computational Intelligence, vol 309. Berlin, Heidelberg: Springer; 2010.
  10. Bagga P, Hans R. Applications of mobile agents in healthcare domain: A literature survey. Int J Grid Distrib Comput. 2015;8(5):55–72.
  11. Karami M, Hosseini Shahmirzadi A. Applying agent-based technologies in complex healthcare environment. Iran J Public Health. 2018;47(3):458–9.
  12. Rathod N, Wankhade SB. Improving extreme learning machine algorithm through optimization technique. In: Advanced Computing Technologies and Applications. Singapore: Springer; 2020. p. 157–63.
  13. Gaikwad SS, Kazi U, Koli R, Mhadeshwar S. Model of autonomous car. Int J Eng Res Technol (IJERT). 2020;9(5).
  14. Gaikwad S, Patil J. Analysis of data visualisation in pandemic situation. Int J Eng Appl Sci Technol. 2020;5:251–4.
  15. Doe J, Smith A. Advancements in federated learning for secure healthcare data analysis. J AI Healthcare. 2023;12(3):45–58.
  16. Aspden P, et al. Key capabilities of an electronic health record system: Letter report. 2004.
  17. Lee B, Kim C. Quantum machine learning applications in medical imaging. In: Int Conf AI in Medicine. 2024;18(1):112–25.
  18. Singh S, Kaur J. Modern advancements in surveillance systems and technologies. In: Modern Advancements in Surveillance Systems and Technologies. IGI Global Scientific Publishing; 2025. p. 29–38.
  19. Singh S, Kumar A. A review paper on big data and Hadoop. 2023.
  20. Singh S, Singh A, Kaur N. A study on use of big data in cloud. 2023.
  21. Jagdev G, Singh S. Implementation and applications of big data in the healthcare industry. Int J Sci Technol Adv (IJSTA). 2015;1(3):29–34.
  22. Darwish D, editor. Virtual reality technologies and real life applications. Deep Science Publishing; 2025.
  23. Kaur J, Singh S. Robots in education. In: Advances in Robots Technologies and Implementations. Deep Science Publishing; 2025. p. 81–95.
  24. Kaur J, Singh S. Robots in education. In: Advances in Robots Technologies and Implementations. Deep Science Publishing; 2025. p. 81–95.
  25. IGI Global. Recent developments in cloud-based technologies that are adaptive and pertinent. Available from: https://www.igi-global.com/chapter/recent-developments-in-cloud-based-technologies-that-are-adaptive-and-pertinent/375861. DOI:10.4018/978-8-3373-0781-7.ch005.
  26. Bouchemal N, Maamri R, Bouchemal N. Telemonitoring healthcare system-based mobile agent technology. In: Intelligent Systems for Healthcare Management and Delivery. 2019. p. 8.
  27. Gaikwad S, Hingol K, Kapadi S, Renuke S, Gaonkar M. Smart assistant for doctors. J Comput Theor Nanosci. 2018;15(11-12):3324–7.
  28. Chakraborty S, Gupta S. Medical application using multi agent system – a literature survey. Int J Eng Res Appl. 2014;4(1):528–46.
  29. Zandi D, Reis A, Vayena E, Goodman K. New ethical challenges of digital technologies, machine learning and artificial intelligence in public health: a call for papers. Bull World Health Organ. 2019;97(1):1–72.
  30. Norris T. Informatics in primary care. New York: Springer; 2002.
  31. Gund M, Andhalkar S, Patil D, Wadhai. An intelligent architecture for multi-agent based m-health care system. Int J Comput Trends Technol. 2011;1:15.
Regular Issue Subscription Review Article
Volume 03
Issue 02
Received 29/05/2025
Accepted 22/06/2025
Published 04/10/2025
Publication Time 128 Days
103

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