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Artificial Intelligence in Healthcare: Opportunities and Challenges

Author(s):Ananya Singh, Rohit Das, Priya Menon

Affiliation: Department of Biomedical Engineering, Manipal Institute of Technology, Karnataka, India

Page No: 28-34

Volume issue & Publishing Year: Volume 2, Issue 7, July 2025

published on: 2025/07/30

Journal: International Journal of Advanced Multidisciplinary Application.(IJAMA)

ISSN NO: 3048-9350

DOI: https://doi.org/10.5281/zenodo.17534456

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Article Indexing:

Abstract:
Artificial Intelligence (AI) is revolutionizing healthcare by enhancing diagnostic accuracy, personalizing treatment, and improving operational efficiencies. This paper reviews the current applications of AI in healthcare, including machine learning in medical imaging, predictive analytics, and robotic-assisted surgeries. The study also addresses challenges such as data privacy, ethical considerations, and integration with existing healthcare systems. Future directions emphasize the need for robust AI models, regulatory frameworks, and multidisciplinary collaboration to fully realize AI�s potential in healthcare.

Keywords: Artificial intelligence, Healthcare applications, Medical diagnostics, Machine learning, Health technology

Reference:

  • 1. Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118.
  • 2. Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., et al. (2018). CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv preprint arXiv:1711.05225.
  • 3. Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., & Sun, J. (2016). Doctor AI: Predicting clinical events via recurrent neural networks. Proceedings of the Machine Learning for Healthcare Conference, 301–318.
  • 4. Yang, G., Zhang, J., Liu, M., & Zhu, L. (2020). Artificial intelligence in robotic surgery: Current status and future perspectives. Artificial Intelligence in Medicine, 103, 101785.
  • 5. Wang, Y., Wang, L., Rastegar-Mojarad, M., Moon, S., Shen, F., Afzal, N., et al. (2019). Clinical information extraction applications: A literature review. Journal of Biomedical Informatics, 77, 34–49.
  • 6. Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care: Addressing ethical challenges. The New England Journal of Medicine, 378(11), 981–983.
  • 7. Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.
  • 8. Liu, X., Faes, L., Kale, A