Tekin Ugur, Dener Murat
Department of Information Security Engineering, Graduate School of Natural and Applied Sciences, Gazi University, Ankara, Türkiye.
Neuroscience and Neurotechnology Center of Excellence (NÖROM), Gazi University, Ankara, Türkiye.
Front Neurol. 2025 Jan 27;16:1474484. doi: 10.3389/fneur.2025.1474484. eCollection 2025.
The incorporation of artificial intelligence (AI) into neuroscience has the potential to significantly enhance our comprehension of brain function and facilitate more effective diagnosis and treatment of neurological disorders. Artificial intelligence (AI) techniques, particularly deep learning and machine learning, offer transformative solutions by improving the analysis of complex neural data, facilitating early diagnosis, and enabling personalized treatment approaches. A bibliometric analysis is a method that employs quantitative techniques for the examination of scientific literature, with the objective of identifying trends in research, evaluating the impact of influential studies, and mapping the networks of collaboration. In light of the accelerated growth and interdisciplinary scope of AI applications in neuroscience, a bibliometric analysis is vital for mapping the landscape, identifying pivotal contributions, and underscoring emerging areas of interest. This study aims to address this need by examining 1,208 studies published between 1983 and 2024 from the Web of Science database. The analysis reveals a notable surge in publications since the mid-2010s, with substantial advancements in neurological imaging, brain-computer interfaces (BCI), and the diagnosis and treatment of neurological diseases. The analysis underscores the pioneering role of countries such as the United States, China, and the United Kingdom in this field and highlights the prevalence of international collaboration. This study offers a comprehensive overview of the current state and future directions of AI applications in neuroscience, as well as an examination of the transformative potential of AI in advancing neurological research and healthcare. It is recommended that future research address the ethical issues, data privacy concerns, and interpretability of AI models in order to fully capitalize on the benefits of AI in neuroscience.
将人工智能（AI）融入神经科学有潜力显著增强我们对大脑功能的理解，并促进对神经系统疾病更有效的诊断和治疗。人工智能（AI）技术，特别是深度学习和机器学习，通过改进对复杂神经数据的分析、促进早期诊断以及实现个性化治疗方法，提供了变革性的解决方案。文献计量分析是一种采用定量技术来审视科学文献的方法，目的是识别研究趋势、评估有影响力研究的影响以及绘制合作网络。鉴于人工智能在神经科学中的应用加速增长且具有跨学科性质，文献计量分析对于描绘该领域的全貌、识别关键贡献以及突出新兴的研究热点至关重要。本研究旨在通过审查来自科学网数据库的1208项在1983年至2024年间发表的研究来满足这一需求。分析显示自2010年代中期以来出版物显著增加，在神经成像、脑机接口（BCI）以及神经系统疾病的诊断和治疗方面取得了重大进展。分析强调了美国、中国和英国等国家在该领域的先驱作用，并突出了国际合作的普遍性。本研究全面概述了人工智能在神经科学中的当前状态和未来方向，以及对人工智能在推进神经科学研究和医疗保健方面的变革潜力的考察。建议未来的研究解决人工智能模型的伦理问题、数据隐私问题以及可解释性，以便充分利用人工智能在神经科学中的益处。
