The application of artificial intelligence (AI) in healthcare has grown rapidly due to technological innovations and the increasing availability of multimodal data. AI, particularly machine learning (ML) and deep learning (DL), supports the analysis and interpretation of medical images, improving diagnostic accuracy. A relevant example is the diagnosis of Eagle syndrome, a rare condition characterized by an elongated styloid process. AI can identify abnormalities in radiological images with high precision, as demonstrated by studies analyzing digital orthopantomographs with 100% accuracy. However, there is still a lack of widespread AI application in the evaluation of Eagle syndrome through computed tomography (CT), a critical diagnostic tool. Additionally, AI, such as ChatGPT, provides support to healthcare professionals and patients by offering information and generating differential diagnoses. ChatGPT can explain Eagle syndrome in a clear and concise manner, but it still has limitations in identifying this rare disease in differential diagnoses. Looking ahead, AI is expected to play a more systematic role in improving the management of rare diseases by creating global databases to facilitate diagnosis and treatment, enhancing support for both professionals and patients.