Are There Any Challenges in Implementing Machine Learning in Cancer Research?
Despite its potential, several challenges exist in implementing ML in cancer research:
Data Quality: Ensuring that data is accurate, complete, and standardized is critical. Data Privacy: Handling sensitive patient data requires stringent privacy measures. Model Interpretability: Understanding how complex models make predictions is crucial for clinical acceptance. Integration with Clinical Workflows: ML tools must be seamlessly integrated into existing medical practices.
