A brief review of practical use of artificial intelligence in surgery in the current era

Shubham Bobade; Sheetal Asutkar; Devesh Nagpure; Amar Kadav

doi:10.31893/multirev.2025085

Keywords:

artificial intelligence (AI)

computational intelligence

computer reasoning

ethics

machine intelligence

Abstract

Artificial Intelligence (AI) is increasingly transforming the field of surgery by enhancing precision, improving patient outcomes, and optimizing surgical workflows. This article explores the practical applications of AI in contemporary surgical practices, including advancements in AI-assisted diagnostic tools, robotic surgery, predictive analytics, and decision-support systems. AI technologies, such as deep learning models and convolutional neural networks, have demonstrated significant improvements in diagnostic accuracy, particularly in medical imaging, pathology, and disease classification. Robotic surgical systems, augmented by AI, offer enhanced precision, control, and dexterity, leading to reduced complications, minimal invasive procedures, and faster recovery times for patients. Predictive analytics powered by AI aids in surgical planning and risk management by forecasting potential complications, personalizing treatment plans, and optimizing surgical outcomes based on patient-specific data. Decision-support systems provide real-time assistance during surgeries, analyzing live data from various sources to offer actionable insights, recommendations, and alerts, thereby enhancing surgical safety and efficiency. Despite these advancements, challenges remain, including the need for diverse and high-quality datasets, ethical considerations regarding data privacy and algorithmic bias, and the seamless integration of AI technologies with existing surgical practices and healthcare systems. In a nutshell, the exciting future of AI in surgery holds great promise and potential for further refinements in surgical techniques, automation, and personalized medicine. Ongoing research, continuous validation, regulatory oversight, and collaboration between AI developers, clinicians, and healthcare policymakers are essential to addressing these challenges and maximizing the benefits of AI in surgical practice, ensuring that these transformative technologies are safe, effective, and accessible to all.
References
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9. Patel, M. S., Desai, M. D., & Malinowski, J. (2021). Enhancing intraoperative safety with AI-driven monitoring systems. Journal of the American College of Surgeons, 233(3), 409-419. https://doi.org/10.1016/j.jamcollsurg.2021.05.018
10. Rao, P. S., Bansal, V., & Manda, S. (2021). Simulation-based surgical planning using AI: Opportunities and challenges. Journal of Surgical Simulation, 8(1), 22-30. https://doi.org/10.1102/2051-7726.2021.0003
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Bobade, S., Asutkar, S., Nagpure, D., & Kadav, A. (2024). A brief review of practical use of artificial intelligence in surgery in the current era. Multidisciplinary Reviews, 8(3), 2025085. https://doi.org/10.31893/multirev.2025085

[1] Esteva, A., Kuprel, B., & Novoa, R. A. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118. https://doi.org/10.1038/nature21056

[2] Knudsen, J. E., Ghaffar, U., Ma, R., & Hung, A. J. (2024). Clinical applications of artificial intelligence in robotic surgery. Journal of Robotic Surgery, 18(1), 102. https://doi.org/10.1007/s11701-024-01867-0

[3] Ghods, A. J., Yang, G., & Hsu, C. Y. (2020). AI-based decision support systems for intraoperative monitoring. Journal of Medical Systems, 44(6), 114. https://doi.org/10.1007/s10916-020-01582-4

[4] Huang, T., Zhang, Y., & Lin, M. (2020). Predictive analytics in surgery: Current status and future directions. Surgical Innovation, 27(1), 43-52. https://doi.org/10.1177/1553350619866362

[5] Jiang, Y., Yu, W., & Sun, W. (2019). Risk prediction models for postoperative complications: A review of AI applications. Journal of Surgical Research, 246, 89-99. https://doi.org/10.1016/j.jss.2019.09.010

[6] Liu, Y., Weiss, S., & Li, L. (2020). Early detection of cancer using AI algorithms: A systematic review. Cancer Research, 80(21), 4905-4915. https://doi.org/10.1158/0008-5472.CAN-20-1325

[7] Mittelstadt, B. D. (2019). Principles of ethical AI: A guide to making AI work for people. AI & Society, 34(2), 373-386. https://doi.org/10.1007/s00146-019-00860-9

[8] Obermeyer, Z., Powers, B., & Vogeli, C. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342

[9] Patel, M. S., Desai, M. D., & Malinowski, J. (2021). Enhancing intraoperative safety with AI-driven monitoring systems. Journal of the American College of Surgeons, 233(3), 409-419. https://doi.org/10.1016/j.jamcollsurg.2021.05.018

[10] Rao, P. S., Bansal, V., & Manda, S. (2021). Simulation-based surgical planning using AI: Opportunities and challenges. Journal of Surgical Simulation, 8(1), 22-30. https://doi.org/10.1102/2051-7726.2021.0003

[11] Sarkar, S., Tzeng, C. H., & Ramesh, R. (2021). Optimizing surgical scheduling and resource allocation with AI: A review. Health Management Technology, 42(5), 34-42. https://doi.org/10.1007/s10916-021-01711-2

[12] Wang, X., Zhao, J., & Li, M. (2021). Advances in robotic surgery and the role of artificial intelligence. Journal of Robotic Surgery, 15(2), 155-164. https://doi.org/10.1007/s11701-020-01090-2

[13] Yang, J., Li, X., & Zhang, X. (2021). AI and robotics: The future of minimally invasive surgery. Annals of Surgery, 273(1), 35-45. https://doi.org/10.1097/SLA.0000000000003606

Abstract

References

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