- Data Quality: The effectiveness of AI depends on the quality and diversity of the training data. Poor-quality data can lead to inaccurate diagnoses. - Interpretability: AI algorithms can be complex and opaque, making it difficult for pathologists to understand how a diagnosis was reached. - Integration: Integrating AI systems into existing clinical workflows can be challenging and may require significant changes in practice. - Regulation and Validation: AI systems must undergo rigorous validation and obtain regulatory approval, which can be a lengthy process.
