What Challenges Are Faced in Implementing Data Analytics?
Despite its advantages, the implementation of data analytics in histology is not without challenges:
Data Quality: The accuracy of analysis depends on the quality of the input data, which can be affected by factors like staining variability and image resolution. Interdisciplinary Collaboration: Effective implementation requires collaboration between histologists, data scientists, and software engineers. Interpretation: Translating complex data outputs into actionable insights requires domain-specific knowledge. Cost: Advanced imaging equipment and analytical software can be expensive.
