Artificial intelligence (AI) in brain tumor classification represents a potentially powerful support for clinicians. It can process large image volume efficiently, detect subtle patterns often imperceptible to the human eye, and provide objective predictions. This capability accelerates and refines the diagnostic process and also reinforces early detection, which is essential for defining the most appropriate treatment plan and improving patient outcomes. In clinical contexts, high accuracy alone is not enough; models must also provide reliable predictions. A critical limitation is overconfidence: deep neural networks give highly certain answers even when wrong, without recognizing the limits of their own knowledge. This results from miscalibration, meaning that model confidence does not reflect actual correctness.