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¹Sağlık Bilimleri Üniversitesi Gülhane Tıp Fakültesi, Dışkapı Yıldırım Beyazıt Eğitim ve Araştırma Hastanesi, Beyin ve Sinir Cerrahisi Kliniği, Ankara, Türkiye
²Gazi Üniversitesi Sağlık Bilimleri Enstitüsü, Biyofizik Doktora Programı, Ankara, Türkiye
³Gazi Üniversitesi Tıp Fakültesi, Biyofizik Anabilim Dalı, Ankara, Türkiye The 20^th century is known as the information age. In parallel with technological developments, patient follow-up, imaging techniques, surgical decision-making and intraoperative methods continued to develop. Artificial intelligence-supported information systems can support the surgeon in terms of screening, surgical decision-making, follow-up, treatment, intraoperative complication management, and postoperative follow-up. Artificial intelligence tries to recognize its environment through the uploaded database and improves itself. In machine learning, the success rate is calculated with the training and test stages. In deep learning, this occurs through layers. In convolutional neural networks, the layers are filtered to reveal the relationship between the inputs. In this way, the relationship between the inputs is revealed. In neurosurgery, artificial intelligence has started to gain a place in many fields through tumor staging, radiotherapy decisions, presence of recurrence, determination of vascular pathologies, determination of the follow-up and prognosis of traumatic brain injury, deep brain stimulation, detection of spondylolisthesis, instability, determination of the needs of intensive care patients and regulation of treatment, and detection of intracranial pressure syndrome. Although the creation of data sets is a long process, artificial intelligence can stand by neurosurgeons in the long run as a cheap, accessible, and reliable method. Anahtar Kelimeler : Deep learning, Convolutional neural networks, Artificial intelligence in neurosurgery, Machine learning