Despite its potential, AI in histology also faces several challenges:
1. Data Quality: The effectiveness of AI depends on the quality and quantity of training data. Poorly annotated or insufficient data can lead to inaccurate results. 2. Integration: Integrating AI systems into existing workflows and laboratory information systems can be complex and costly. 3. Interpretability: Understanding how AI arrives at its conclusions can be difficult, making it challenging to trust and validate its findings. 4. Regulatory Approval: AI tools must undergo rigorous validation and obtain regulatory approval, which can be a lengthy process. 5. Ethical Concerns: There are ethical issues related to data privacy, consent, and the potential for AI to replace human jobs.
