Journal article
Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery
World neurosurgery: X, v 23, 100301
01 Jul 2024
PMID: 38577317
Abstract
Neurosurgeons receive extensive technical training, which equips them with the knowledge and skills to specialise in various fields and manage the massive amounts of information and decision-making required throughout the various stages of neurosurgery, including preoperative, intraoperative, and postoperative care and recovery. Over the past few years, artificial intelligence (AI) has become more useful in neurosurgery. AI has the potential to improve patient outcomes by augmenting the capabilities of neurosurgeons and ultimately improving diagnostic and prognostic outcomes as well as decision-making during surgical procedures. By incorporating AI into both interventional and non-interventional therapies, neurosurgeons may provide the best care for their patients. AI, machine learning (ML), and deep learning (DL) have made significant progress in the field of neurosurgery. These cutting-edge methods have enhanced patient outcomes, reduced complications, and improved surgical planning.
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Details
- Title
- Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery
- Creators
- Wireko Andrew Awuah - Sumy State UniversityFavour Tope Adebusoye - Sumy State UniversityJack Wellington - Cardiff UniversityLian David - University of East AngliaAbdus Salam - Khyber Teaching HospitalAmanda Leong Weng Yee - University of Malaya, Kuala Lumpur, MalaysiaEdouard Lansiaux - Université de LilleRohan Yarlagadda - Rowan UniversityTulika Garg - Government Medical College and HospitalToufik Abdul-Rahman - Sumy State UniversityJacob Kalmanovich - Drexel UniversityGoshen David Miteu - University of NottinghamMrinmoy Kundu - Institute of Medical Sciences and Sum HospitalNikitina Iryna Mykolaivna - Sumy State University
- Publication Details
- World neurosurgery: X, v 23, 100301
- Publisher
- Elsevier; AMSTERDAM
- Number of pages
- 9
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- College of Medicine
- Web of Science ID
- WOS:001349309800001
- Scopus ID
- 2-s2.0-85189021955
- Other Identifier
- 991021901413204721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Clinical Neurology
- Surgery