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Can artificial intelligence overtake human intelligence on the bumpy road towards glioma therapy?
Precilla S Daisy1, T S Anitha2,3
1Central Inter-Disciplinary Research Facility, School of Biological Sciences, Sri Balaji Vidyapeeth (Deemed to-be University), Pillaiyarkuppam, Puducherry, India.
Medical Oncology (Northwood, London, England)
|April 3, 2021
Summary
Artificial intelligence (AI) shows promise in diagnosing and managing gliomas, a type of brain tumor. However, challenges in AI reliability and transparency currently limit its widespread clinical use in neuro-oncology.
Area of Science:
- Neuro-oncology
- Medical Imaging
- Artificial Intelligence
Background:
- Gliomas are aggressive primary brain tumors posing significant clinical management challenges.
- Current diagnostic methods rely on manual interpretation of imaging data, which is time-consuming.
- The blood-brain barrier limits chemotherapy effectiveness against gliomas.
Purpose of the Study:
- To review the clinical utility of artificial intelligence (AI) in glioma management.
- To identify barriers hindering the implementation of AI in neuro-oncology.
- To explore AI's potential in improving glioma diagnosis and treatment planning.
Main Methods:
- Review of current literature on AI applications in neuro-oncology.
- Analysis of AI's role in glioma grading, imaging analysis, and outcome prediction.
- Discussion of AI's limitations, including reliability and transparency issues.
Main Results:
- AI demonstrates promising performance in glioma diagnosis, grading, and differentiating tumors from healthy tissue.
- AI can potentially streamline the analysis of magnetic resonance imaging and computed tomography data.
- Despite advancements, AI's reliability and transparency remain significant concerns for clinical adoption.
Conclusions:
- AI offers a revolutionary approach to glioma management, enhancing personalized medicine.
- Addressing AI's "black-box" nature and ensuring reliability are crucial for its successful integration into neuro-oncology.
- Further research is needed to overcome implementation barriers and fully realize AI's potential in brain tumor treatment.
