Despite the advancements, several challenges persist:
1. Data Quality: Inconsistent or incomplete data can lead to inaccurate risk predictions. 2. Bias: Models may inherit biases present in the training data, leading to unequal risk assessments across different populations. 3. Interpretability: Complex models, especially those using machine learning, can be difficult to interpret and explain. 4. Privacy Concerns: The use of sensitive medical and genetic data raises privacy and ethical concerns.
