Vol. 15 No. 3 (2023): BAPIOAC23 - National Conference

Artificial Intelligence in Medicine: Friend or Foe

JS Bamrah
University of Bolton, UK
Raj Kumar
Kleyn and Integral Health Group, 5 Allen Street, Warrington, WA2 7JD 
Shekhar Kapur
Mumbai, India
sushruta cover showing a regal building at night

Published 2023-10-03


  • artificial intelligence,
  • healthcare

How to Cite

Bamrah, J., Kumar, R., & Kapur, S. (2023). Artificial Intelligence in Medicine: Friend or Foe. Sushruta Journal of Health Policy & Opinion, 15(3). https://doi.org/10.38192/15.3.18


Artificial Intelligence (AI) has been utilised in many settings, including in the film industry where it is already changing several aspects of how storylines are conceived (1). Similarly, medical advancements are now seeing a major shift in how AI might influence patient treatments, sometimes controversially so. We explore the transformative role of AI in the field of medicine, highlighting its potential to improve diagnostic accuracy, treatment efficacy, and patient outcomes. We highlight various applications of AI in medical imaging, diagnosis, drug discovery, personalised medicine, decision support systems, prevention, and patient engagement. Furthermore, we discuss the ethical and legal considerations and challenges associated with AI integration in healthcare. Drawing on relevant research and case studies, this paper provides insights into the future implications of AI in medicine.


  1. Shekhar Kapur's 'Masoom' Gets a Twist with AI's Help (ts2.space)
  2. 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.
  3. Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature medicine, 25(1), 44-56.
  4. Suleyman, M., Worrall, D., & Thomas, E. (2018). Applications of machine learning in human health and how AI is currently used in healthcare.
  5. Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., ... & Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama, 316(22), 2402-2410.
  6. Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317-1318.
  7. Challen, R., Denny, J., Pitt, M., Gompels, L., Edwards, T., & Tsaneva-Atanasova, K. (2019). Artificial intelligence, bias and clinical safety. BMJ health & care informatics, 26(1), e000079.
  8. Hautaniemi, S., Kovanen, L., & Alkkiomaki, T. (2021). Artificial intelligence in healthcare operations: Capabilities, applications, and challenges. Computers in Industry, 127, 103426.
  9. Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature medicine, 25(1), 44-56.
  10. Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358.