Evaluating the Potential of ChatGPT in CBCT Reporting: Are we Stepping into the Future?

ABSTRACT Introduction: Radiology has undergone a significant transformation, becoming an essential component of modern medicine. Artificial Intelligence (AI), including tools like Chat Generative Pre-trained Transformer (ChatGPT), has demonstrated the ability to revolutionize workflows in dentistry and healthcare. ChatGPT, a large language model (LLM) trained on extensive datasets, can assist in diagnostic processes by generating CBCT reports. Materials and Methods: The study included 50 cases of intraosseous pathologies with CBCT and histopathology as primary investigations. Cases were evaluated by two oral radiologists to generate a primary radiographic diagnosis (PRD1) and differential diagnoses (DD). ChatGPT was then prompted to analyze the same cases, generating a second report (R2) with PRD2 and DD. The performance of ChatGPT was evaluated based on three criteria: comparison of PRD2 with PRD1, accuracy against the histopathologic diagnosis (gold standard), and quality of the generated reports as rated by radiologists on a Likert scale. Results: ChatGPT-generated reports matched the primary radiographic diagnosis of radiologists in 64% of cases (Likert score: 4.24/5). The accuracy of PRD2 in matching histopathologic diagnosis was 62%, with a Likert score of 4.2/5. Regarding quality, 72% of ChatGPT reports were rated excellent (Likert score: 4.5/5). However, oral radiologists' accuracy in diagnosing pathologies was higher (88%) compared to ChatGPT (62%). ChatGPT performed better in cases where minimal clinical information was required but struggled in cases demanding detailed history or clinical context. Conclusion: Artificial intelligence, including ChatGPT, has shown promise as an assistant to oral radiologists in generating CBCT reports. While ChatGPT achieved satisfactory results in diagnostic accuracy and report quality, its performance in clinical scenarios requiring complex reasoning remains limited. This study highlights the potential of AI tools in radiology while emphasizing the need for further improvements to match the expertise of experienced radiologists.