Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: May 18, 2024
Open Peer Review Period: May 24, 2024 - Jul 19, 2024
Date Accepted: Aug 3, 2024
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Artificial intelligence in dental radiology: improving the efficiency of reporting with ChatGPT – a comparative study
ABSTRACT
Background:
Structured and standardized documentation is critical for accurately recording diagnostic findings, treatment plans, and patient progress in healthcare. 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 in generating radiology reports from dental panoramic radiographs (OPG), comparing the performance of AI-generated reports with those manually created by dental students.
Methods:
One hundred dental students were tasked with analyzing OPGs 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. Clinical relevance: This underscores the need for further refinement in the AI’s prompt design and the development of robust validation mechanisms to enhance its utility in clinical settings.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.