Generative Artificial Intelligence, With Constrained Information, Outperforms Pre-Doctoral Student Average on Oral Pathology Differential Diagnosis Questions.

IF 1.9 4区 教育学 Q3 DENTISTRY, ORAL SURGERY & MEDICINE
Austin J Davies
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引用次数: 0

Abstract

Background: Artificial intelligence (AI) technologies have seen rapid advancement and are increasingly used in healthcare fields, including clinical diagnostics and dental education. Despite their growing prominence, their effectiveness in assisting clinical decision-making in dental education remains under-explored. This study examined the performance of Generative AI in generating a clinical impression for oral pathology cases relative to dental students.

Aims: The aim of this experiment was to assess the diagnostic accuracy and potential difference of Generative AI in clinical oral pathology compared to that of Doctor of Dental Surgery (DDS) students.

Methods: A clinical oral pathology differential diagnosis exam was administered to both an AI model and DDS students. The AI model received limited information about each case, while the DDS students were provided with standard case details and a multiple-choice selection. The accuracy and statistical significance between both groups were compared and evaluated.

Results: The AI model displayed higher diagnostic accuracy compared to the students, 95.65% to 78.92%, respectively, and the difference in groups was statistically significant.

Conclusion: The findings suggest that Generative AI has the potential to be a valuable tool in clinical oral pathology, even when provided with minimal case information. Its superior diagnostic performance compared to DDS students highlights prospective benefits of incorporating AI into dental education and specifically in helping students formulate clinical impressions.

具有约束信息的生成式人工智能在口腔病理鉴别诊断问题上优于博士生平均水平。
背景:人工智能(AI)技术发展迅速,越来越多地应用于医疗保健领域,包括临床诊断和牙科教育。尽管他们日益突出,他们的有效性,协助临床决策在牙科教育仍未充分探讨。本研究考察了生成人工智能在口腔病理病例中相对于牙科学生产生临床印象的表现。目的:本实验的目的是评估生成式人工智能在口腔临床病理诊断中的准确性和与牙科外科医生(DDS)学生的潜在差异。方法:对人工智能模型和DDS学生进行口腔临床病理鉴别诊断检查。人工智能模型接收到关于每个案例的有限信息,而DDS学生提供的是标准案例细节和多项选择题。比较并评价两组间的准确性及统计学意义。结果:AI模型诊断准确率高于学生,分别为95.65% ~ 78.92%,组间差异有统计学意义。结论:研究结果表明,即使提供了最少的病例信息,生成人工智能也有可能成为临床口腔病理学的有价值的工具。与DDS学生相比,其优越的诊断性能突出了将人工智能纳入牙科教育的潜在好处,特别是在帮助学生形成临床印象方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.10
自引率
16.70%
发文量
127
审稿时长
6-12 weeks
期刊介绍: The aim of the European Journal of Dental Education is to publish original topical and review articles of the highest quality in the field of Dental Education. The Journal seeks to disseminate widely the latest information on curriculum development teaching methodologies assessment techniques and quality assurance in the fields of dental undergraduate and postgraduate education and dental auxiliary personnel training. The scope includes the dental educational aspects of the basic medical sciences the behavioural sciences the interface with medical education information technology and distance learning and educational audit. Papers embodying the results of high-quality educational research of relevance to dentistry are particularly encouraged as are evidence-based reports of novel and established educational programmes and their outcomes.
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