Artificial Intelligence in Neurosurgery: Shaping the Future of Precision Neurosurgical Care

To the Editor: We appreciate the response to our article on Chatbots in Neurosurgery and the authors' discussion on the ethical issues surrounding the use of artificial intelligence (AI).1,2 It is a unique experience to discuss the ethics of using AI generated by another AI and an excellent example of both the benefits and risks of incorporating AI into a collaborative discussion forum. The authors have aptly highlighted several key concerns that need careful consideration in the integration of AI into patient care and research practices, and we recognize that as this technology evolves, more and more questions and ultimately regulations will evolve as well. While the above letter1 appropriately discusses several ethical issues surrounding the use of AI, there are additional benefits and risks that can arise in the context of neurosurgical research, writing, and patient care. We recognize that this concept is in evolution. As we collaborate more with future updated iterations of AI, we must remember additional risks and benefits and keep in mind that in its current form, this technology does not eliminate the important and unique human contribution to its remarkable abilities. In addition, negative results due to this technology continue to remain a human problem. Enhanced Precision: Future iterations of AI can assist in neurosurgical procedures by providing real-time guidance and precise mapping, which can improve accuracy and reduce potential human errors. Data Analysis and Pattern Recognition: AI algorithms can analyze vast amounts of patient data, including medical images, clinical records, and research literature, to identify patterns and correlations that may not be readily apparent to human researchers. This can potentially lead to new insights and personalized treatment approaches. Predictive Analytics: AI has the potential to predict patient outcomes, complications, and responses to treatment based on historical data, aiding in decision-making and risk assessment. Ethical Challenges in Decision-Making: As AI systems become more complex, there is a potential for conflicts between the decisions made by AI algorithms and human health care providers. The responsibility and accountability for critical decisions may become blurred, requiring careful consideration and clear guidelines. Overreliance on AI: While AI can be a valuable tool, overreliance on AI-generated recommendations or diagnoses may lead to a diminished role for human judgment, critical thinking, and the individualized approach that is essential in neurosurgical care and critical to the current best use of this technology. Lack of Understanding: The intricate workings of AI algorithms can be challenging for health care professionals to grasp fully. This lack of transparency and interpretability may create a barrier in understanding and trusting the recommendations provided by AI systems. It is important to demystify the AI black box, helping stakeholders comprehend the decisions made by these algorithms. Data Bias and Generalizability: Biases present in training data can carry over to AI models, potentially resulting in biased predictions or treatments. To err is human, but to err with bias is AI's cardinal sin. Ensuring diverse and representative data sets is critical to avoid disparities and ensure fairness in patient care. It is important for researchers, health care providers, and policymakers to remain vigilant and continually explore both the benefits and risks of AI implementation in neurosurgical research, writing, and patient care. Striking the right balance between AI assistance and human expertise is crucial for delivering optimal health care outcomes while upholding ethical standards. In its current form, AI remains a tool to be used by humans. As such, our own ethics still apply.

Artificial intelligence and robotics in global neurosurgery: A scoping review.

Background:Artificial intelligence (AI) and machine learning (ML) have significantly advanced medical diagnostics and surgical procedures, particularly in spinal and cranial surgery. Robotic-assisted surgery has emerged as a transformative approach, offering increased precision, reduced intraoperative complications, and improved surgical outcomes. This review examines the effectiveness and reliability of AI in spinal and cranial diagnosis, its integration into robotic-assisted surgical interventions, and the associated ethical concerns.Method:An extensive literature search was search was conducted on different search engines such as PubMed, Google Scholar, and Scopus to find relevant articles.Result:Findings suggest that AI models exhibit high accuracy in detecting spinal and cranial pathologies. ML algorithms contribute to enhanced prognostic assessments and decision-making in neurosurgery. Robotic-assisted surgeries have superior accuracy, lower radiation exposure, and fewer postoperative complications compared to conventional methods. However, challenges such as data biases, lack of transparency in AI decision-making, regulatory hurdles, and the high costs of AI-driven interventions pose significant barriers to widespread adoption. Ethical concerns, including patient privacy, algorithmic bias, and the potential overreliance on AI, must be addressed to ensure responsible integration into clinical practice.Conclusion:Use of AI and machine learning improves the diagnostic outcomes and decreases post op complications in the field of spinal and cranial surgery. But certain challenges such as ethical concerns and technical hurdles should be sorted out with effective planning. Further research is necessary to refine AI-driven interventions, enhance cost-effectiveness, and to make sure ethical and equitable implementation of AI and robotic surgery in neurosurgical care.

Chapter 20 - Artificial intelligence as applied to clinical neurological conditions

Introduction: The advancement of artificial intelligence (AI) in neurosurgery is dependent on high quality, large, labeled datasets. Labeled neurosurgical datasets are rare, driven by the high expertise required for labeling neurosurgical data. A comprehensive resource overviewing available datasets for AI in neurosurgery is essential to identify areas for potential model building and areas of needed data construction. Methods: We conducted a systematic review according to PRISMA guidelines to identify publicly available neurosurgical datasets suitable for machine learning. A PubMed search on 8 February 2025, yielded 267 articles, of which 86 met inclusion criteria. Each study was reviewed to extract dataset characteristics, model development details, validation status, availability, and citation impact. Results: Among the 86 included studies, 83.7% focused on spine pathology, with tumor (3.5%), vascular (4.7%), and trauma (7.0%) comprising the remaining. The majority of datasets were image-based, particularly X-ray (37.2%), MRI (29.1%), and CT (20.9%). Label types included segmentation (36.0%), diagnosis (26.7%), and detection/localization (20.9%), with only 2.3% including outcome labels. While 97.7% of studies reported training a model, only 22.6% performed external validation, 20.2% shared code, and just 7.1% provided public applications. Accuracy was the most frequently reported performance metric, even for segmentation tasks, where only 60% of studies used the Dice score metric. Studies often lacked task-appropriate evaluation metrics. Conclusions: We conducted a systematic review to capture all publicly accessible datasets that can be applied to build AI models for neurosurgery. Current datasets are heavily skewed towards spine imaging and lack both clinical patient specific and outcomes information. Provided baseline models from these datasets are limited by poor external validation, lack of reproducibility, and reliance on suboptimal evaluation metrics. Future efforts should prioritize developing multi-institutional datasets with outcome labels, validated models, public access, and domain diversity to accelerate the safe and effective integration of AI into neurosurgical care.

Artificial intelligence (AI) has revolutionized various industries, and health care is no exception. With the advent of AI, neurosurgical critical care has witnessed significant advancements in the diagnosis, treatment, and management of critical brain disorders. AI has the potential to transform the way health care providers approach and manage critical care patients, especially those with severe brain injuries.[1]

The Future of Sustainable Neurosurgery: Is a Moonshot Plan for Artificial Intelligence and Robot-Assisted Surgery Possible in Japan?

Applications of Artificial Intelligence in Neurosurgery for Improving Outcomes Through Diagnostics, Predictive Tools, and Resident Education.

Breaking boundaries in neurosurgery through art and technology: A historical perspective

Emerging from the foundational efforts of the Lancet Commission on Global Surgery (2015) and the Bogota Declaration (2016), the Boston Declaration 2025 marks a transformative milestone in the Global Neurosurgery movement. It calls for equitable, timely, and affordable neurosurgical care across all regions, especially in low-resource settings. Central to this vision is the active inclusion of Neuroscience Nurses—recognized as frontline caregivers, educators, and advocates—within the neurosurgical nexus. The Boston Declaration uniquely positions Neuroscience Nurses as central architects in this transformation. Nurses serve as essential links across the continuum of care, contributing to frontline service delivery, capacity building, education, policy advocacy, and health system integration. Their inclusion ensures a person-centered approach rooted in lived experience and local context. The Declaration grounded in for an expanded neurosurgical ecosystem that embraces interdisciplinary collaboration—spanning clinicians, engineers, policymakers, and communities—and encourages innovation through data, artificial intelligence, and sustainability. It underscores the urgency of building resilient care systems to address the global neurosurgical burden. As a global blueprint for neurosurgical equity, the Boston Declaration invites all Neuroscience Nurses and professional societies to take an active role in this collective endeavour. Through shared commitment, we can drive transformative progress that reshapes global neurosurgical care and affirms the essential role of nurses in leading change.

BackgroundDigital neurosurgery represents a transformative shift in modern neurosurgical practice, integrating advanced technologies, such as three-dimensional (3D) imaging, robotics, artificial intelligence (AI), and digital twin technology (DTT) models. These technologies offer innovative solutions for preoperative planning, intraoperative navigation, and postoperative management, with an emphasis on precision, personalization, and efficiency.MethodsWe conducted a scoping review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Scoping Review (PRISMA-ScR) checklist and guidance from the Joanna Briggs Institute (JBI) Manual for Evidence Synthesis. PubMed, Web of Science (WOS), and China National Knowledge Infrastructure (CNKI) were searched without language or date limits through September 2025. Eligibility was structured using the Population–Concept–Context (PCC) framework. Two reviewers independently screened records in Rayyan with consensus resolution, and data were charted using a prepiloted form. A total of 133 sources were included and mapped.ResultsKey technologies reviewed include: (1) 3D reconstruction: Facilitates precise anatomical modeling, improving spatial understanding and surgical planning. (2) 3D printing (3DP): Enables creation of patient-specific models and surgical guides, enhancing preoperative simulation and intraoperative accuracy. (3) Digital twins (DT): Offers dynamic virtual models for real-time surgical simulation, training, and personalized patient management. (4) Intraoperative navigation: Utilizes advanced electromagnetic and AI-enhanced systems to improve tracking accuracy and reduce surgical errors. (5) Robotic-assisted surgery: Includes telesurgical, supervisory, and handheld systems that enhance precision and enable minimally invasive procedures. (6) AI: Supports image registration, subtask automation, and clinical decision-making, improving diagnostic and prognostic accuracy. These technologies demonstrate significant benefits in operative precision, patient outcomes, training efficacy, and interdisciplinary communication, though challenges remain in data integration, regulatory standards, and computational demands.ConclusionPositioning this study as a scoping review clarifies its objective to map technologies and applications across digital neurosurgery rather than to synthesize effect estimates, thereby providing an evidence-informed overview to guide future systematic evaluations. Digital neurosurgery is rapidly evolving toward greater integration of multimodal data, real-time adaptive systems, and AI-driven automation. Future developments should focus on standardizing regulatory frameworks, enhancing data fusion capabilities, and promoting interdisciplinary collaboration to fully realize the potential of digital technologies in advancing neurosurgical care.

The pediatric brain represents a dynamic biological target characterized by rapid myelination and functional reorganization, which presents unique challenges for conventional, adult-centric artificial intelligence (AI) models. This review provides a structured overview of the evolution of AI applications in pediatric neuroimaging and neurosurgery, tracing the transition from early standardized pipelines and handcrafted imaging biomarkers to contemporary deep learning-based approaches for segmentation, prediction, and anomaly detection. Recent advances indicate a paradigm shift from static image interpretation toward dynamic and interactive intelligence, in which AI systems actively support clinical decision-making during surgery rather than functioning solely as diagnostic tools. This new paradigm is supported by four technological domains : brain foundation models designed to capture age-aware neurodevelopmental representations; spatial computing technologies for three-dimensional, context-aware-visualization; physical AI systems integrating robotic safety constraints; and multimodal AI agents that act as cognitive surgical copilots by synthesizing imaging, physiological, and intraoperative data in real time. By shifting the role of AI from preoperative assessment to intraoperative guidance, this paradigm offers new opportunities to enhance surgical precision, safety, and workflow efficiency in pediatric neurosurgery. This review aims to provide neurosurgeons with a conceptual framework for understanding and adopting next-generation AI technologies that align with the dynamic nature of the developing brain and the clinical demands of pediatric neurosurgical care.

Maximal safe surgical resection is a foundational principle in brain tumor surgery. To date, many intraoperative modalities have been developed to help facilitate the identification of brain tumor versus normal brain tissue so that surgical resection is maximized but limited to the boundaries of the tumor for preservation of neurological function. Of note, Raman spectroscopy has been adapted into one of these modalities because of its ability to provide rapid, non-destructive, label-free intraoperative evaluation of tumor borders and molecular classifications and help guide surgical decision-making in real time. In this review, we performed a literature review of the landmark studies incorporating Raman spectroscopy into neurosurgical care to highlight its current applications and limitations. In this modern day, Raman spectroscopy is able to detect tumor cells intraoperatively for primary glial neoplasms, meningiomas, and brain metastases with greater than 90% accuracy. For glioma surgery, a major recent advancement is the ability to detect different mutations intraoperatively, specifically IDH, 1p19q co-deletion, and ATRX, given their implications on survival and how much extent of resection should be ideally achieved. With recent advancements in artificial intelligence and their integration into stimulated Raman histology, many of these tasks can be completed in as fast as ~10 s and on average 2-3 min. Despite the incorporation of artificial intelligence, spectral data can still be heavily influenced by background noise, and its preprocessing has significant variability across platforms, which can impact the accuracy of results. Overall, Raman spectroscopy has significantly changed the intraoperative workflow of brain tumor surgery, and this review highlights the capabilities that neurosurgeons can currently take advantage of in their practice, the existing data to support it, and the areas that researchers can further optimize to improve accuracy and patient outcomes.

Opportunities and Considerations for the Incorporation of Artificial Intelligence into Global Neurosurgery: A Generative Pretrained Transformer Chatbot-Based Approach

Precision neurosurgery is rapidly evolving as a medical specialty by merging genomic medicine, multi-omics technologies, and artificial intelligence (AI) technology, while at the same time, society is shifting away from the traditional, anatomic model of care to consider a more precise, molecular model of care. The general purpose of this review is to contemporaneously reflect on how these advances will impact neurosurgical care by providing us with more precise diagnostic and treatment pathways. We hope to provide a relevant review of the recent advances in genomics and multi-omics in the context of clinical practice and highlight their transformational opportunities in the existing models of care, where improved molecular insights can support improvements in clinical care. More specifically, we will highlight how genomic profiling, CRISPR-Cas9, and multi-omics platforms (genomics, transcriptomics, proteomics, and metabolomics) are increasing our understanding of central nervous system (CNS) disorders. Achievements obtained with transformational technologies such as single-cell RNA sequencing and intraoperative mass spectrometry are exemplary of the molecular diagnostic possibilities in real-time molecular diagnostics to enable a more directed approach in surgical options. We will also explore how identifying specific biomarkers (e.g., IDH mutations and MGMT promoter methylation) became a tipping point in the care of glioblastoma and allowed for the establishment of a new taxonomy of tumors that became applicable for surgeons, where a change in practice enjoined a different surgical resection approach and subsequently stratified the adjuvant therapies undertaken after surgery. Furthermore, we reflect on how the novel genomic characterization of mutations like DEPDC5 and SCN1A transformed the pre-surgery selection of surgical candidates for refractory epilepsy when conventional imaging did not define an epileptogenic zone, thus reducing resective surgery occurring in clinical practice. While we are atop the crest of an exciting wave of advances, we recognize that we also must be diligent about the challenges we must navigate to implement genomic medicine in neurosurgery-including ethical and technical challenges that could arise when genomic mutation-based therapies require the concurrent application of multi-omics data collection to be realized in practice for the benefit of patients, as well as the constraints from the blood-brain barrier. The primary challenges also relate to the possible gene privacy implications around genomic medicine and equitable access to technology-based alternative practice disrupting interventions. We hope the contribution from this review will not just be situational consolidation and integration of knowledge but also a stimulus for new lines of research and clinical practice. We also hope to stimulate mindful discussions about future possibilities for conscientious and sustainable progress in our evolution toward a genomic model of precision neurosurgery. In the spirit of providing a critical perspective, we hope that we are also adding to the larger opportunity to embed molecular precision into neuroscience care, striving to promote better practice and better outcomes for patients in a global sense.