Keyword: Perioperative Care
1 result found.
Review Article
Epidemiology and Health Data Insights, 2(4), 2026, ehdi043, https://doi.org/10.63946/ehdi/18696
ABSTRACT:
Artificial intelligence (AI) is transforming the practice of surgery through the support of clinical decision-making, improved accuracy and efficiency, and better patient outcomes across the perioperative period. This review evaluates and critically analyzes current, technological and evidence-based applications of AI in surgery. Machine learning, deep learning, computer vision and natural language processing are some of the core AI technologies that are enabling developments in all phases of surgery. Some of these applications include, improved preoperative diagnosis, intraoperative real-time imaging, workflow analysis, robotic surgery assistance and intraoperative clinical decision support. Postoperatively, the potential of AI is also vast. Through the use of predictive models for surveillance and early complications detection, as well as remote management of patients, AI is optimizing postoperative care.
Although the findings suggest promise for the future, the clinical adoption of AI in surgery is limited by a number of issues, including data quality and heterogeneity, lack of validation in large prospective studies, potential for bias, ethical concerns and high costs of implementation. Other factors including clinician acceptance, data privacy, regulatory approval and medico-legal implications need to be addressed before any new technology is widely adopted.
Future directions of AI in surgery includes progression to semi-autonomous systems that augments the efforts of the surgeon, integration with emerging technologies such as genomics and digital twins, and increased use in low resource settings to help address existing global inequities in surgical care. For successful AI adoption in surgery, AI systems will need to undergo robust validation, require interdisciplinary approaches, and all systems must be developed in a manner that is not only transparent but also clinically appropriate for surgeons, so that it can maximise the benefits of human talent in the operating room while maintaining patient safety.
Although the findings suggest promise for the future, the clinical adoption of AI in surgery is limited by a number of issues, including data quality and heterogeneity, lack of validation in large prospective studies, potential for bias, ethical concerns and high costs of implementation. Other factors including clinician acceptance, data privacy, regulatory approval and medico-legal implications need to be addressed before any new technology is widely adopted.
Future directions of AI in surgery includes progression to semi-autonomous systems that augments the efforts of the surgeon, integration with emerging technologies such as genomics and digital twins, and increased use in low resource settings to help address existing global inequities in surgical care. For successful AI adoption in surgery, AI systems will need to undergo robust validation, require interdisciplinary approaches, and all systems must be developed in a manner that is not only transparent but also clinically appropriate for surgeons, so that it can maximise the benefits of human talent in the operating room while maintaining patient safety.
