Artificial intelligence (AI) aims to replicate human cognitive abilities, such as problem-solving and decision-making. Early AI relied on logical reasoning and rule-based systems, but advancements led to machine learning (ML), where models learn from data to make predictions. Deep learning (DL), a subset of ML, addresses the challenges of handling unstructured data by utilizing artificial neural networks (ANNs), particularly deep neural networks (DNNs). AI is transforming medical diagnostics, especially in endoscopy, imaging (X-rays, MRI, CT scans), and waveform analysis (ECGs). In MRI-based preoperative diagnosis of endometrial cancer, AI enhances tumor detection, myometrial invasion assessment, and segmentation accuracy. In some cases, AI models even outperform radiologists. Additionally, AI-assisted pathology using hematoxylin and eosin (H&E)-stained slides improves histopathological diagnosis and molecular classification of endometrial cancer, aiding treatment decisions. AI-based hysteroscopy is also enhancing diagnostic accuracy. As AI continues to evolve, its integration into medical imaging, pathology, and real-time diagnostics is revolutionizing healthcare and cancer detection.