What Challenges Exist in Implementing Machine Learning in Histology?
Despite its potential, there are several challenges:
1. Data Quality: High-quality, annotated data is essential for training ML models. Inconsistent or incomplete data can lead to inaccurate results. 2. Interpretability: Understanding how ML models arrive at their conclusions can be difficult, making it hard for clinicians to trust and validate the results. 3. Integration: Integrating ML tools into existing clinical workflows can be complex and requires significant investment in infrastructure and training. 4. Ethical Concerns: Ensuring patient privacy and addressing biases in the data are crucial ethical considerations.
