ArticleSurgical neurology international2026
Artificial intelligence in neurosurgery: A patent bibliometric analysis of the Lens database.
Article in Surgical neurology international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: 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. Methods: 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. Results: 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. Conclusion: 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.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.