Binary trees are a basic type of data structure. They are useful, but they also have some problems that can make them less effective. Here are a few key challenges that come with using binary trees:

1. Imbalance Problems:

   • Sometimes, binary trees can become unbalanced. When this happens, it takes longer to search, add, or remove items. In the worst case, these operations can take as long as checking every item, which is called $O(n)$.

2. Binary Search Trees (BSTs):

   • Binary search trees are a kind of binary tree that can work quickly when they are balanced. On average, they can do tasks in $O(\log n)$ time if they are set up well. But, if we keep adding items one after another, they can become unbalanced and slow down to $O(n)$.

3. Need for Self-Balancing:

   • To fix the imbalance, we need more advanced types of trees, like AVL trees or Red-Black trees. These trees help keep everything balanced, but they also make things a bit more complicated, especially when we're adding or removing items.

Possible Solutions:

1. Using Self-Balancing Trees:

   • By using AVL trees or Red-Black trees, we can address the imbalance problems effectively. They have strict rules that help keep things balanced, so operations can still take about $O(\log n)$ time.

2. Adding Extra Structures:

   • We can also combine binary trees with other structures like heaps or tries to make them work better. However, this can make things more complicated and might take up more resources.

In summary, while binary trees are important for creating many data structures, their problems mean we often need to use more advanced solutions to manage data efficiently.