Advancing Brain Tumor Detection Using Machine Learning And Artificial Intelligence: A Systematic Literature Review Of Predictive Models And Diagnostic Accuracy

Abstract

This study systematically reviews the application of Artificial Intelligence (AI) and Machine Learning (ML) in brain tumor detection, focusing on advancements, challenges, and clinical implications. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 1,126 articles were initially identified, with rigorous screening and quality assessments narrowing the final selection to 102 high-quality studies. The review highlights the superior diagnostic accuracy and efficiency achieved by AI models, particularly Convolutional Neural Networks (CNNs), which consistently report accuracy rates exceeding 90%. Hybrid and ensemble models further enhance diagnostic robustness, addressing challenges related to complex tumor types and heterogeneous datasets. Data-related issues, such as scarcity and imbalance, remain critical barriers, with studies emphasizing the effectiveness of synthetic data generation and augmentation techniques in improving model generalizability. Explainable AI (XAI) frameworks have been identified as pivotal for fostering clinician trust, offering interpretability and transparency that facilitate integration into clinical workflows. Real-time diagnostic systems demonstrate the potential for AI to streamline operations and enable timely clinical decisions, particularly in resource-constrained settings. Despite these advancements, challenges such as algorithmic bias, data diversity, and infrastructural limitations persist. This review underscores the transformative role of AI/ML in brain tumor diagnostics, providing actionable insights to advance research and clinical adoption, ultimately improving patient outcomes and healthcare efficiency.