What Are the Challenges of Implementing AutoML in Histology?

Despite its advantages, implementing AutoML in histology comes with its own set of challenges. These include:

Data Quality: The quality of histological images and data is crucial for the accuracy of AutoML models.
Computational Resources: AutoML processes can be resource-intensive, requiring significant computational power and storage.
Integration: Integrating AutoML systems with existing histological workflows can be complex and time-consuming.
Regulatory Compliance: Ensuring that AutoML systems comply with medical regulations and standards is essential for their adoption in clinical settings.