TY  - JOUR
AU  - Stephan, Daniel
AU  - Bertsch, Annika
AU  - Burwinkel, Matthias
AU  - Vinayahalingam, Shankeeth
AU  - Al-Nawas, Bilal
AU  - Kämmerer, Peer W
AU  - Thiem, Daniel GE
PY  - 2024
DA  - 2024/12/23
TI  - AI in Dental Radiology—Improving the Efficiency of Reporting With ChatGPT: Comparative Study
JO  - J Med Internet Res
SP  - e60684
VL  - 26
KW  - artificial intelligence
KW  - ChatGPT
KW  - radiology report
KW  - dental radiology
KW  - dental orthopantomogram
KW  - panoramic radiograph
KW  - dental
KW  - radiology
KW  - chatbot
KW  - medical documentation
KW  - medical application
KW  - imaging
KW  - disease detection
KW  - clinical decision support
KW  - natural language processing
KW  - medical licensing
KW  - dentistry
KW  - patient care
AB  - Background: Structured and standardized documentation is critical for accurately recording diagnostic findings, treatment plans, and patient progress in health care. Manual documentation can be labor-intensive and error-prone, especially under time constraints, prompting interest in the potential of artificial intelligence (AI) to automate and optimize these processes, particularly in medical documentation. Objective: This study aimed to assess the effectiveness of ChatGPT (OpenAI) in generating radiology reports from dental panoramic radiographs, comparing the performance of AI-generated reports with those manually created by dental students. Methods: A total of 100 dental students were tasked with analyzing panoramic radiographs and generating radiology reports manually or assisted by ChatGPT using a standardized prompt derived from a diagnostic checklist. Results: Reports generated by ChatGPT showed a high degree of textual similarity to reference reports; however, they often lacked critical diagnostic information typically included in reports authored by students. Despite this, the AI-generated reports were consistent in being error-free and matched the readability of student-generated reports. Conclusions: The findings from this study suggest that ChatGPT has considerable potential for generating radiology reports, although it currently faces challenges in accuracy and reliability. This underscores the need for further refinement in the AI’s prompt design and the development of robust validation mechanisms to enhance its use in clinical settings. 
SN  - 1438-8871
UR  - https://www.jmir.org/2024/1/e60684
UR  - https://doi.org/10.2196/60684
DO  - 10.2196/60684
ID  - info:doi/10.2196/60684
ER  -