A narrative review on artificial intelligence in neurosurgery: ethical challenges and implementation considerations

Abstract

Introduction: Artificial intelligence (AI) is revolutionizing neurosurgery by enhancing diagnostic precision, surgical planning, and postoperative management. However, its integration raises ethical concerns related to bias, privacy, accountability, and the potential dehumanization of healthcare. This review focuses on navigating these challenges while maximizing AI's potential in improving patient care. Methodology: A narrative review was conducted by identifying studies from PubMed, Cochrane Library, and Google Scholar databases. The search utilized the following keywords: "artificial intelligence," "neurosurgery," "machine learning," "data privacy," "robotic surgery," "ethics," and "bias." The review primarily focused on issues of dataset bias, data privacy, and the need for transparency and accountability in clinical decision-making. Results and critical insights: AI significantly improves diagnostic accuracy and the management of neurological conditions; however, it also poses risks, such as exacerbating healthcare disparities and compromising patient data security. Recommended strategies include the development of ethical frameworks, inclusion of diverse datasets, and fostering surgeon-AI collaboration to ensure equitable outcomes. Conclusion: AI holds immense promise in enhancing neurosurgical diagnostics, surgical planning, and postoperative care. Nonetheless, its responsible integration demands robust ethical and regulatory frameworks that prioritize patient safety, transparency, and equity. Interdisciplinary collaboration and continuous real-world validation remain essential to address ongoing clinical and ethical challenges as AI technologies evolve.