CHALLENGES AND LIMITATIONS OF UTILIZING ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS OF BRAIN TUMORS

Abstract

The utilization of Artificial Intelligence (AI) in medical diagnostics has grown exponentially, offering significant advances in the accuracy and efficiency of diagnosing conditions such as brain tumors. However, the integration of AI into clinical settings is not without its challenges and limitations. This paper critically examines the primary obstacles and constraints faced when applying AI technologies in the diagnosis of brain tumors. One major challenge is the diversity and complexity of brain tumor types, which can affect the training and performance of AI models. The variability in tumor presentation requires highly sophisticated algorithms capable of recognizing subtle nuances in imaging data, which may not be uniformly available across different datasets. Another significant hurdle is the acquisition of high-quality, annotated datasets necessary for training AI systems. These datasets are often limited due to privacy concerns, the rarity of certain tumor types, and the sheer expense of data collection and curation. Furthermore, the integration of AI tools into healthcare workflows raises ethical and legal concerns, particularly regarding patient consent and the transparency of AI decision-making processes. The risk of algorithmic bias also poses a substantial limitation, potentially leading to disparities in healthcare outcomes among different patient demographics. This paper suggests that addressing these challenges requires a multidisciplinary approach involving continuous collaboration between data scientists, clinicians, and legal experts to ensure that AI tools are safe, effective, and equitable in their application. This examination not only highlights the technological and regulatory hurdles but also paves the way for developing robust AI systems that can reliably assist in the early detection and diagnosis of brain tumors, ultimately improving patient outcomes.