Enhancing Brain Tumour Diagnosis with Artificial Intelligence: A Systematic Review of Technological Advancements and Future Directions
Main Article Content
Abstract
This review systematically explores recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) for the diagnosis of brain tumors, with an emphasis on gliomas. The review highlights the critical limitations of traditional diagnostic methods, including their invasiveness, time consumption, and the requirement for high specialization, which contribute to variable diagnostic outcomes. It details how AI and ML enhance the accuracy, efficiency, and non-invasiveness of brain tumor diagnostics by leveraging complex algorithms and vast datasets to analyze medical images, surpassing the capabilities of human interpretation. Key advancements discussed include the integration of AI with conventional imaging techniques such as MRI, CT scans, and PET, where AI algorithms significantly reduce human error, enhance diagnostic precision, and facilitate earlier and more accurate tumor identification. Additionally, the review assesses the impact of AI on improving diagnostic processes and highlights significant technological and methodological innovations in AI that have led to breakthroughs in medical imaging. It also identifies current gaps in AI applications and suggests future research directions. By offering a comprehensive evaluation of AI's role in the diagnostic landscape, this review underscores AI’s potential to transform brain tumor diagnostics, thereby enhancing patient outcomes and optimizing healthcare processes.
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