What Are the Challenges in Implementing Machine Learning?
1. Data Quality and Quantity: High-quality, annotated datasets are crucial for training effective ML models. Obtaining large, diverse datasets can be challenging. 2. Interpretability: Understanding how ML algorithms make decisions is essential for clinical applications. Black-box models, where decision-making processes are not transparent, can be problematic. 3. Integration with Existing Workflows: Incorporating ML tools into current histological practices requires careful planning and training for pathologists.
