What Are the Challenges in Automated Data Analysis?
Despite its potential, automated data analysis in cancer research faces several challenges: - Data Quality: Inconsistent or incomplete data can lead to inaccurate analyses. - Computational Complexity: Analyzing large datasets requires significant computational power and sophisticated algorithms. - Interpretability: Understanding the results generated by complex models can be difficult, limiting their clinical utility. - Data Integration: Combining data from different sources (e.g., genomic and clinical data) poses technical and standardization challenges.
