ChatGPT-4.0 in oral and maxillofacial radiology: prediction of anatomical and pathological conditions from radiographic images - Shila Kahalian; Marieh Rajabzadeh; Melisa Öçbe; Mahmut Sabri Medisoglu (Folia Medica)
Introduction: ChatGPT has the ability to generate human-like text, analyze and understand medical images using natural Language processing (NLP) algorithms. It can generate real-time diagnosis and recognize patterns and learn from previous cases to improve accuracy by combining patient history, symptoms, and image characteristics. It has been used recently for learning about maxillofacial diseases, writing and translating radiology reports, and identifying anatomical landmarks, among other things. Materials and methods: In this study, 52 radiographic images were queried on the OpenAI application ChatGPT-4.0. The responses were evaluated with and without using clues for specific radiographs to see if adding clues during prompting improved diagnostic accuracy. Results: The true prediagnosis rate without any clue was 30.7%. By adding one clue this rate significantly increased to 56.9%. There was not a significant difference in accurate diagnosis of anatomical landmarks, cysts, and tumors (p>0.05). However, including internal structure information improved the diagnostic accuracy (p<0.05) Conclusion: GPT-4.0 showed a tendency to misdiagnose closely located anatomical structures and by adding additional clues its performance showed improvement, while its ability to recognize diverse differential diagnoses remains limited.