• Journal of dental hygiene science
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• v.24 no.1
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• pp.62-70
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• 2024

Background: This study aimed to evaluate ChatGPT's performance accuracy in responding to questions from the national dental hygienist examination. Moreover, through an analysis of ChatGPT's incorrect responses, this research intended to pinpoint the predominant types of errors. Methods: To evaluate ChatGPT-3.5's performance according to the type of national examination questions, the researchers classified 200 questions of the 49th National Dental Hygienist Examination into recall, interpretation, and solving type questions. The researchers strategically modified the questions to counteract potential misunderstandings from implied meanings or technical terminology in Korea. To assess ChatGPT-3.5's problem-solving capabilities in applying previously acquired knowledge, the questions were first converted to subjective type. If ChatGPT-3.5 generated an incorrect response, an original multiple-choice framework was provided again. Two hundred questions were input into ChatGPT-3.5 and the generated responses were analyzed. After using ChatGPT, the accuracy of each response was evaluated by researchers according to the types of questions, and the types of incorrect responses were categorized (logical, information, and statistical errors). Finally, hallucination was evaluated when ChatGPT provided misleading information by answering something that was not true as if it were true. Results: ChatGPT's responses to the national examination were 45.5% accurate. Accuracy by question type was 60.3% for recall and 13.0% for problem-solving type questions. The accuracy rate for the subjective solving questions was 13.0%, while the accuracy for the objective questions increased to 43.5%. The most common types of incorrect responses were logical errors 65.1% of all. Of the total 102 incorrectly answered questions, 100 were categorized as hallucinations. Conclusion: ChatGPT-3.5 was found to be limited in its ability to provide evidence-based correct responses to the Korean national dental hygiene examination. Therefore, dental hygienists in the education or clinical fields should be careful to use artificial intelligence-generated materials with a critical view.

• Journal of the korean academy of Pediatric Dentistry
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• v.37 no.2
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• pp.193-201
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• 2010

Early childhood caries (ECC) is a comprehensive terminology that includes nursing bottle caries and rampant dental caries occurred in infants and children. In previous studies, ECC was thought to affect body growth of children negatively. The purpose of this study was to evaluate the effect of ECC on body growth of children in respect of their chronologic age and degree of dental caries. Height and body weight were used as means for physical growth measurements. Children, who visited the pediatric department of Yonsei University Dental Hospital, received oral and physical examinations, and they were divided into the control and ECC groups. Then, each group was subdivided according to their age and gender. Two-sample T test was used to compare the mean height and body weight of the control and ECC groups, and Likelihood Ratio Chi-square test was used to compare their growth percentile distribution. When the mean height and weight were compared, there was a common tendency observed even though statistical significance was not found in all cases. Before the age of 3-4, the mean height and weight tended to be greater in the ECC groups compared to the control groups, whereas after the age of 3-4, the mean height and weight of the ECC group tended to be less compared to the control group. In addition, in groups with age equal or greater than 3-4, which presented significant difference in height and body weight, the percentage of children showing less than 3 percentile growth was greater in the ECC group than the control group. These results imply the negative effects of the ECC on physical growth of the infants and children, and its effects on physical growth may present different characteristics according to chronologic age of the patients.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.127-146
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• 2022

Recently, as word embedding has shown excellent performance in various tasks of deep learning-based natural language processing, researches on the advancement and application of word, sentence, and document embedding are being actively conducted. Among them, cross-language transfer, which enables semantic exchange between different languages, is growing simultaneously with the development of embedding models. Academia's interests in vector alignment are growing with the expectation that it can be applied to various embedding-based analysis. In particular, vector alignment is expected to be applied to mapping between specialized domains and generalized domains. In other words, it is expected that it will be possible to map the vocabulary of specialized fields such as R&D, medicine, and law into the space of the pre-trained language model learned with huge volume of general-purpose documents, or provide a clue for mapping vocabulary between mutually different specialized fields. However, since linear-based vector alignment which has been mainly studied in academia basically assumes statistical linearity, it tends to simplify the vector space. This essentially assumes that different types of vector spaces are geometrically similar, which yields a limitation that it causes inevitable distortion in the alignment process. To overcome this limitation, we propose a deep learning-based vector alignment methodology that effectively learns the nonlinearity of data. The proposed methodology consists of sequential learning of a skip-connected autoencoder and a regression model to align the specialized word embedding expressed in each space to the general embedding space. Finally, through the inference of the two trained models, the specialized vocabulary can be aligned in the general space. To verify the performance of the proposed methodology, an experiment was performed on a total of 77,578 documents in the field of 'health care' among national R&D tasks performed from 2011 to 2020. As a result, it was confirmed that the proposed methodology showed superior performance in terms of cosine similarity compared to the existing linear vector alignment.