Artificial intelligence‐driven surgical innovation: A catalyst for medical equity

Dr. Takeuchi and Kitagawa1 described the importance of artificial intelligence (AI) in surgical innovation. AI is rapidly gaining ground in various surgical fields worldwide. The current trajectory indicates that harnessing AI technologies can significantly improve patient care by reinforcing established practices and accelerating surgical innovation, offering a distinctive chance to explore potential advantages in providing health services to low- and middle-income countries (LMICs) globally. Large language models hold immense promise in revolutionizing medical education and emerging as indispensable assets in surgical practice. Medical students and surgeons could easily access a wealth of educational materials and clinical insights presented intuitively, enriching their understanding and proficiency.2 AI technologies have demonstrated their effectiveness in tailoring surgical training, streamlining administrative duties, and creating practical and affordable simulation training programs tailored to the specific needs of diverse individuals.3 Integrating machine learning algorithms in areas like big data analysis, computer vision, and operative robotics promises to revolutionize surgical patient risk assessment, surgical treatment, and postoperative monitoring, potentially enhancing patient outcomes through reductions in morbidity and mortality.4 More importantly, immediate intra-operative suggestions can assist surgeons in providing better evidence-based treatment to surgical patients. As the key players in this transformation, surgeons have the power to grasp the basic principles of AI, understand its implications in healthcare, and explore avenues for integrating this technology. Collaboration with data scientists to capture comprehensive data and provide clinical context is pivotal to optimizing surgical care quality. In the upcoming AI-driven era, it is crucial to prioritize AI's conscientious and ethical utilization. This should be underscored by vigilant monitoring of data governance and potential patient safety risks during deployment. The implementation process must also address usability, pathway feasibility, and the crucial need for thorough evaluation of healthcare technology and evidence generation. When these may be perceived as obstacles to AI adoption, holistic implementation strategies promise to establish a robust framework for the widespread integration of AI across healthcare systems, ensuring its responsible and ethical use. Global surgery encompasses a swiftly growing interdisciplinary domain dedicated to enhancing and ensuring fair access to quality surgical care within global healthcare frameworks. Initiatives within global surgery predominantly concentrate on bolstering capabilities, advocating for equitable access, facilitating educational programs, conducting research, and crafting policies tailored to the context of all countries. This is particularly significant for LMICs, where AI can potentially address deficiencies in surgical, anesthetic, and postoperative care, and is estimated to prevent 18 million mortalities annually, mainly in LMICs.5 It holds promise in providing insights for governance, infrastructure development, and logistics prediction, bolstering the foundational pillars of global surgery. The expansion of the interdisciplinary domain of AI in surgery brings with it a beacon of hope, aiming to bridge the gap in access to equitable quality of surgical care globally. Further research is imperative to ensure optimal and equitable AI utilization, overcoming obstacles such as data representativeness, training deficiencies, human hesitancy, and ethical considerations. Dealing with these challenges through a targeted, evidence-driven strategy could assist LMICs in surmounting bureaucratic inefficiencies and establishing improved surgical systems. N/A. The authors declare no conflicts of interest for this article. Approval of the research protocol: N/A. Informed consent: N/A. Registry and the Registration No. of the study/trial: N/A. Animal studies: N/A.