Artificial intelligence in neurosurgery: A patent bibliometric analysis of the Lens database

Artificial intelligence (AI) has rapidly expanded across neurosurgery with applications in neuroimaging analysis, surgical planning, intraoperative guidance, neuromodulation, and outcome prediction. Bibliometric analyses of scholarly works cited by patents provide a unique method for identifying research that has influenced technological development. This study aimed to identify and characterize the most patent-cited scholarly publications related to AI in neurosurgery.

A bibliometric analysis was performed using the Lens database, which links global patent records with scholarly literature. A systematic search using predefined neurosurgical and machine learning terms yielded 72,719 publications, filtered to 4663 works cited by patents spanning 1962-2025. Publications were ranked by patent citation count as a proxy for technological influence. The 50 most patent-cited publications were analyzed by publication year, journal, institutional affiliation, and geographic origin.

The top 50 publications were published between 1994 and 2020. Most (41 of 50; 82%) were published between 2010 and 2020, reflecting rapid growth in AI-related neurosurgical research. Earlier influential works focused on image-guided surgery and augmented reality visualization, whereas more recent publications increasingly involved machine learning-based neuroimaging analysis, intraoperative diagnostics, neuromodulation optimization, and spine surgery applications.

AI-related innovation in neurosurgery has accelerated substantially over the past decade. Patent citation analysis highlights the importance of interdisciplinary collaboration in translating computational advances into neurosurgical technologies and provides insight into research most closely associated with technological innovation in the field.