ReviewAnnals of medicine and surgery (2012)2025
A narrative review on artificial intelligence in neurosurgery: ethical challenges and implementation considerations.
Review in Annals of medicine and surgery (2012), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artificial intelligence in brain tumor diagnosis and surgical planning: Recent advances.Surgical neurology international · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.