What Are the Challenges of Implementing Machine Learning in Cancer Care?
Despite its potential, there are several challenges in implementing ML in cancer care:
Data Quality and Quantity: High-quality, labeled data is essential for training ML models, but such data is often scarce or inconsistent. Interpretability: Many ML models, especially deep learning, operate as "black boxes," making it difficult to understand how they arrive at specific decisions. Integration: Integrating ML solutions into existing healthcare systems and workflows requires significant effort and coordination. Ethical Considerations: Issues related to data privacy, security, and ethical use of AI in healthcare must be carefully managed.
