What Are the Challenges of Using Azure ML in Cancer Research?
Despite its advantages, there are several challenges associated with using Azure ML in cancer research:
Data Privacy: Ensuring the confidentiality of patient data is crucial. Compliance with regulations like HIPAA is necessary. Data Quality: The accuracy of machine learning models heavily depends on the quality of the input data. Inconsistent or incomplete data can lead to incorrect conclusions. Interpretability: Many machine learning models, particularly deep learning models, act as "black boxes," making it difficult to understand how they arrive at their predictions. Integration: Integrating machine learning models with existing clinical workflows can be challenging and requires careful planning.
