Abstract
BACKGROUND AND OBJECTIVE: Artificial intelligence (AI) is transforming surgical care by enhancing clinical decision-making and providing intraoperative guidance. As its applications expand, ethical challenges arise, including algorithmic bias, transparency in AI reasoning, informed consent regarding AI involvement, and accountability surrounding AI-guided decisions. This review explores the readiness of the surgical community to address these issues at both the institutional and individual levels. METHODS: A PubMed search identified literature on AI in surgery published between 2018-2025. Fourteen key studies were selected and reviewed to assess AI applications across the surgical continuum, with attention to ethical considerations and barriers to integration. KEY CONTENT AND FINDINGS: AI now supports surgical care from the preoperative diagnosis through postoperative recovery. AI can outperform or match clinician performance in tumor detection, disease diagnosis, and surgical risk stratification. Predictive models using deep learning can outperform traditional scoring systems for perioperative and postoperative complication risk. Intraoperatively, AI enables surgical phase recognition, augmented reality guidance, and detection of technical errors. Despite these benefits, ethical concerns remain. Algorithmic bias may underestimate the needs of marginalized populations. Furthermore, questions of legal liability arise when AI-guided decisions cause harm. Informed consent must now address AI's role, limitations, and potential consequences if declined. Surgeons must guard against "automation bias" to preserve human judgment and patient trust. Institutional readiness remains unsatisfactory, as many healthcare systems lack infrastructure for real-time data integration and governance over data ownership. Surgeon skepticism and the "black box" nature of models also hinder adoption of the technology. Education on AI's design, validation, and biases is essential for safe integration. CONCLUSIONS: While AI holds immense potential to enhance surgical care, its use should be grounded in ethical principles to ensure non-maleficence and justice. Adoption should aim to maximize beneficence while preserving patient autonomy through transparent consent and promoting equity in access and implementation. At the same time, surgeons must remain vigilant against automation bias such that AI supports, not replaces, clinical intuition and trust, which lies at the core of the surgeon-patient relationship.