Current challenges of the state-of-the-art of AI techniques for diagnosing brain tumor

Abstract

Background: Brain is the control center of the human body, in recent time, different variety of brain diseases are being discovered. The brain disease diagnosis tools are becoming challenging and still an open area of research, application of AI in brain disease diagnosis has made disease prediction and detection more precise and accurate. Automated technologies for non-invasive analysis of brain images have become necessary, because disease of brain is fatal and are the cause of large number of deaths in developed countries. Brain tumor surgery augmented with AI can result in safer and more effective treatment. The knowledge gap between clinical and data science experts still presents significant challenges. This paper will review literatures related to current challenges of AI technologies for brain tumor diagnosis and suggest new directions of AI technologies for diagnosing brain tumour. A systematic search of major academic databases (such as Science Direct, IEEE explore digital Library, and Google scholar) was conducted to identify relevant studies published between 2015 and 2023. The search term used in this study include “Brain tumor Diagnosis”, “AI challenges in Brain tumor Diagnosis”, ‘AI techniques” and “AI challenges in medicine and future”. Studies were included if they utilized AI techniques for brain tumor diagnosis. The identified studies were evaluated for the key challenges they encountered in their diagnostic approaches. The Present study identified several challenges related to the application of AI techniques in brain tumour diagnosis. These challenges include: Interpretability and explainability, variations in tumour location, shape, and size which make accurate segmentation and classification difficult. Overall, the challenges in explaining brain tumor detection stem from the unique requirements and complexities of the healthcare domain, necessitating specialized techniques and approaches. This study summarizes the new directions for AI as (I) Data Hungry: Large, standardized, annotated data sets and excellent ground truth data are necessary for the development of accurate AI. (II) Radiomics: makes it possible to extract a vast number of quantitative features from intricate clinical imaging arrays and convert them into high-dimensional data that can be further processed to determine their relationship to the histological features of the tumor, which represent underlying genetic mutations and malignancy as well as grade, progression, response to therapy, and even overall survival (OS). (III) Black box: AI, for instance, is capable of predicting the best course of care for a patient, but it is unable to explain its reasoning. A trend toward easing this restriction is interpretable deep learning, (IV) Demonstrating the generalizability of deep learning applications and conducting external validation are two major obstacles. (V) There are knowledge gaps in clinical oncology that need to be filled in order to successfully integrate AI and maximize its effects. (VI) Several national professional bodies have started programs to bridge these knowledge gaps and advance the adoption of AI in oncology in response to these difficulties.