Background: We evaluated the capability of an AI application to independently detect, segment and classify intracranial tumors in MRI. Methods: In this retrospective single-centre study, 138 patients (65 female and 73 male) with a mean age of 35 +- 26y were included. 97 were diagnosed with an intracranial neoplasm, while 41 exhibited no intracranial pathology. Inclusion criteria were a 1.5 or 3.0T MRI dataset with the following sequences: T2 axial, T1 axial pre- and post-contrast with a slice thickness between 3 and 6mm and no previous brain surgery. Image analysis was performed by two human readers (R1 5 5 years and R2 5 10 years of experience in brain MRI) and a deep learning (DL)-based AI model. Sensitivity, specificity and accuracy of the AI model and the human readers to detect and correctly classify brain tumors were measured. Histological results served as the gold standard. Results: The AI model reached a sensitivity of 93.81% [87.02-97.70] and a specificity of 63.41% [46.94-77.88], while human readers reached 100% [96.27-100.00] and 100% [91.40-100.00], respectively. Human readers provided a significantly higher accuracy rate with R1 0.93 (95% CI: 0.88-0.97) and R2 0.98 (95% CI: 0.94, 0.99) vs. 0.74 (95% CI: 0.66-0.81) for the AI model (P-value <0.001). Conclusion: The underlying DL-based AI algorithm can independently identify and segment intracranial tumors while providing satisfactory results for establishing the correct diagnosis. Despite its current inferiority compared to experienced radiologists, it still experiences ongoing development and it is a step towards developing an artificial intelligence-augmented radiology workflow. Kulcsszavak: brain tumor, artificial intelligence, automated classification, MRI