Big changes in how we predict health outcomes can greatly improve care for patients with cancer. But there are still some big challenges to face:

• Data Quality: The success of these predictions relies on having good data. Unfortunately, sometimes this data is hard to find, biased, or not complete.

• Complexity of Disease: Cancer is complicated. Many factors can influence it. Because of this, models that predict health outcomes can sometimes make things too simple, leading to wrong predictions.

• Integration with Clinical Practice: It's tough to use these predictions in real-life healthcare. There aren't clear and standard ways to do this yet.

To tackle these problems, we need to keep working on:

• Improving Data Collection: We should find better ways to gather and access patient data to improve its quality.

• Developing Robust Models: We can use machine learning to notice complicated patterns without making them too simple.

• Training Clinicians: It's important to prepare healthcare professionals to understand and use these predictions correctly.