Artificial Intelligence in the Management of Glioma: Era of Personalized Medicine - Sotoudeh, Houman; Shafaat, Omid; Bernstock, Joshua D.; Brooks, Michael David; Elsayed, Galal A.; Chen, Jason A.; Szerip, Paul; Chagoya, Gustavo; Gessler, Florian; Sotoudeh, Ehsan; Shafaat, Amir; Friedman, Gregory K. (Frontiers in Oncology, 2019)
Purpose: Artificial intelligence (AI) has accelerated novel discoveries across multiple disciplines including medicine. Clinical medicine suffers from a lack of AI-based applications, potentially due to lack of awareness of AI methodology. Future collaboration between computer scientists and clinicians is critical to maximize the benefits of transformative technology in this field for patients. To illustrate, we describe AI-based advances in the diagnosis and management of gliomas, the most common primary central nervous system (CNS) malignancy. Methods: Presented is a succinct description of foundational concepts of AI approaches and their relevance to clinical medicine, geared toward clinicians without computer science backgrounds. We also review novel AI approaches in the diagnosis and management of glioma. Results: Novel AI approaches in gliomas have been developed to predict the grading and genomics from imaging, automate the diagnosis from histopathology, and provide insight into prognosis. Conclusion: Novel AI approaches offer acceptable performance in gliomas. Further investigation is necessary to improve the methodology and determine the full clinical utility of these novel approaches.