人工智能、机器学习和深度学习在神经外科中应用的最新成果和挑战 - 回顾人工智能在神经外科中的应用

Q1 Medicine
Wireko Andrew Awuah , Favour Tope Adebusoye , Jack Wellington , Lian David , Abdus Salam , Amanda Leong Weng Yee , Edouard Lansiaux , Rohan Yarlagadda , Tulika Garg , Toufik Abdul-Rahman , Jacob Kalmanovich , Goshen David Miteu , Mrinmoy Kundu , Nikitina Iryna Mykolaivna
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引用次数: 0

摘要

神经外科医生接受过广泛的技术培训,掌握了各领域的专业知识和技能,能够在神经外科手术的各个阶段(包括术前、术中、术后护理和恢复)管理所需的大量信息和决策。在过去几年中,人工智能(AI)在神经外科中的作用越来越大。人工智能可以增强神经外科医生的能力,最终改善诊断和预后结果以及手术过程中的决策,从而改善患者的治疗效果。通过将人工智能融入介入和非介入疗法,神经外科医生可以为患者提供最好的治疗。人工智能、机器学习(ML)和深度学习(DL)在神经外科领域取得了重大进展。这些前沿方法提高了患者的治疗效果,减少了并发症,改善了手术规划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery

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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来源期刊
World Neurosurgery: X
World Neurosurgery: X Medicine-Surgery
CiteScore
3.10
自引率
0.00%
发文量
23
审稿时长
44 days
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