Can artificial intelligence overtake human intelligence on the bumpy road towards glioma therapy?

Advancements in Oncology with Artificial Intelligence—A Review Article

“…This function should take as input a set of features that describe the data and output a prediction corresponding to the learning task. Classical ML algorithms are generally good at approximating linear or simple non-linear functions [ 13 , 26 ].…”

“…As a result, clinicians have to manually compile all the data for validation in order to formulate a treatment plan. In this regard, AI has proven to be useful in the diagnosis and management of CNS malignancies [ 26 ].…”

“…High-grade tumors such as GBM have a rapid proliferation rate and invade the surrounding regions beyond the enhancing regions on the radiological images, and excision of these areas could be missed [ 26 , 113 ]. AI-based DL algorithms have been developed to facilitate the surgeons to remove maximum tumor regions and less of the normal healthy brain tissue simultaneously.…”

“…The ability of CNN models to differentiate the true progression from pseudo-progression and ML algorithms to differentiate radiation necrosis from tumor recurrence is revolutionary [ 109 , 110 , 118 ]. Additionally, CNN and SVM create a superior model to predict the treatment response and survival outcomes from clinical, imaging, genetic, and molecular marker data [ 26 ].…”

Artificial Intelligence in Brain Tumour Surgery—An Emerging Paradigm

“…Additionally, existing methods of histopathological diagnosis rely on human visual pattern recognition and analysis of cellular morphological features; despite means of standardisation, this inherently introduces bias due to the subjective nature and differences in judgement between different histopathologists [ 150 , 151 ]. AI stands to disrupt this process, and promises to result in faster, more accurate diagnoses, with more uniform standardization [ 152 ]. ML for histological analysis has made significant progress over the last decade [ 153 ].…”