Machine Learning in Predicting Surgical Outcomes in Neurosurgery

Machine learning has emerged as a transformative force in the field of neurosurgery, offering innovative tools to predict surgical outcomes with greater accuracy and efficiency. Traditional prognostic models rely heavily on clinical judgment and general statistical associations, which may not capture the complexity and interactivity of patient-specific factors influencing surgical results. While recent years have seen an explosion in applications of artificial intelligence and machine learning in medicine, their integration into neurosurgical prognostication is still evolving. Current research highlights the potential for machine learning to analyze large and multidimensional clinical, imaging, and genomic datasets, identifying patterns and predictors that conventional methods may overlook. Despite these advancements, challenges remain in model generalizability, interpretability, ethical considerations, and the translation of predictive insights into clinical practice.This Research Topic aims to explore and critically assess the development, validation, and clinical deployment of machine learning algorithms in predicting outcomes for neurosurgical procedures. Central questions include which types of machine learning models yield the most reliable predictions, how these tools can augment clinical decision making, and what barriers exist to their widespread adoption in neurosurgical practice. By addressing these issues, the collection seeks to foster interdisciplinary dialogue and stimulate the generation of robust, clinically actionable predictive models.To gather further insights in the intersection of machine learning and neurosurgical outcome prediction, we welcome articles addressing, but not limited to, the following themes:- Development and benchmarking of machine learning models for neurosurgical prognosis- Integration of clinical, radiological, and omics data in outcome prediction- Model interpretability and explainability in neurosurgical applications- Prospective and multicenter validations of predictive modelsClinical decision support tools and real-world implementation challenges- Ethical, legal, and regulatory considerations in AI use for neurosurgery- Patient engagement and shared decision-making aided by AI predictions Topic editors: Zach Pennington, Jay J. Park, Mario Ganau Source: https://www.frontiersin.org/research-topics/81300/machine-learning-in-predicting-surgical-outcomes-in-neurosurgery