What Are the Challenges in Implementing Machine Learning in Cancer?
Several challenges exist in implementing ML in cancer research, including:
- Data Privacy: Ensuring the confidentiality of patient data is crucial. - Data Quality: Incomplete or noisy data can lead to inaccurate predictions. - Model Interpretability: Complex models like deep learning are often seen as "black boxes," making it difficult to understand how decisions are made. - Clinical Integration: Bridging the gap between ML research and clinical practice is essential for real-world applications.
