Open AccessAre thinking machines breaking new frontiers in neuro-oncology? A narrative review on the emerging role of machine learning in neuro-oncological practice
Author(s) -
Mustafa Mushtaq Hussain,
Ainsia Shabbir,
Saqib Kamran Bakhshi,
Muhammad Shahzad Shamim
Publication year - 2021
Publication title -
asian journal of neurosurgery
Language(s) - English
Resource type - Journals
ISSN - 2248-9614
DOI - 10.4103/ajns.ajns_265_20
Subject(s) - medicine , artificial intelligence , narrative review , machine learning , narrative , clinical practice , variable (mathematics) , big data , computer science , medical physics , data science , intensive care medicine , data mining , mathematical analysis , philosophy , linguistics , mathematics , family medicine
Medical science in general and oncology in particular are dynamic, rapidly evolving subjects. Brain and spine tumors, whether primary or secondary, constitute a significant number of cases in any oncological practice. With the rapid influx of data in all aspects of neuro-oncological care, it is almost impossible for practicing clinicians to remain abreast with the current trends, or to synthesize the available data for it to be maximally beneficial for their patients. Machine-learning (ML) tools are fast gaining acceptance as an alternative to conventional reliance on online data. ML uses artificial intelligence to provide a computer algorithm-based information to clinicians. Different ML models have been proposed in the literature with a variable degree of precision and database requirements. ML can potentially solve the aforementioned problems for practicing clinicians by not just extracting and analyzing useful data, by minimizing or eliminating certain potential areas of human error, by creating patient-specific treatment plans, and also by predicting outcomes with reasonable accuracy. Current information on ML in neuro-oncology is scattered, and this literature review is an attempt to consolidate it and provide recent updates.