Analysis of Teaching Effectiveness of the Combinations of Artificial Intelligence Technology with PBL and CBL in Clinical Dermatology Cosmetology Teaching

Analysis of Teaching Effectiveness of the Combinations of Artificial Intelligence Technology with PBL and CBL in Clinical Dermatology Cosmetology Teaching | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analysis of Teaching Effectiveness of the Combinations of Artificial Intelligence Technology with PBL and CBL in Clinical Dermatology Cosmetology Teaching Li Zeng, Yihao Wang, Wanxing Liao, Yiping Wang, Xiangping Xu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5307839/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose This study aimed to evaluate the effectiveness of integrating artificial intelligence (AI) with problem-based learning (PBL) and case-based learning (CBL) in a dermatology cosmetology course for clinical medical interns. Materials & Methods This prospective randomized controlled trial involved 70 clinical interns rotating in the medical cosmetology department. Participants were randomly assigned to two groups and followed the same curriculum over 8 weeks. The experimental group (n = 40) used a teaching method combining AI with PBL and CBL, while the control group (n = 30) followed the traditional PBL and CBL approach. Assessments included theoretical exams, case analysis, and anonymous feedback surveys to evaluate teaching quality. Results All participants completed the examinations and questionnaires. The average theoretical test scores and case analysis test scores of the experimental group were higher than those of the control group (P < 0.001). The indicators of the experimental group’s feedback were better than those of the control group, such that the improvement of learning interest and motivation, the improvement of understanding of diseases and knowledge, the improvement of independent learning ability, the improvement of clinical thinking and summary ability, as well as a more positive classroom atmosphere and more helpful for future clinical work (P < 0.05). Conclusion Compared to the PBL combined CBL teaching method, the teaching mode combining AI with PBL and CBL teaching method showed a higher efficacy. The learning model effectively improved students’ outcomes and satisfaction, which helped students narrow the gap between theoretical knowledge and clinical practical application. Figures Figure 1 Figure 2 Figure 3 1. INTRODUCTION As educational models diversify, advanced medical education faces growing challenges in effectively engaging students. Cosmetic dermatology, a branch of medicine focused on skin science and guided by medical aesthetics, studies the function and structure of human skin to maintain, enhance, and promote skin health and beauty. 1 Cosmetic dermatology is a very practical subject, the theoretical knowledge on the book is a summary of the general law of skin science by medical experts, but the symptoms and manifestations of each patient are different. Integrating theoretical knowledge with clinical practice is essential, as treatments must be tailored to each patient’s specific condition. 2 Traditional lecture-based teaching, where knowledge is passively transmitted from teacher to student, does little to develop students' critical thinking or practical application skills. 3 Additionally, issues like subjective errors, unclear images, and lack of understanding often arise. Recently, the rapid advancement of AI has led to the increasing use of AI-driven skin detection systems in cosmetic dermatology, offering valuable support for clinical education. 4 Unlike traditional atlas-based teaching, AI can analyze vast amounts of skin image data through machine learning, 5 accurately identifying and quantifying features like pigmentation, wrinkles, erythema, and oiliness, helping to standardize and improve patient consultation. This enhances the precision and efficiency of diagnosing cosmetic skin conditions and guiding treatment. 6 AI technology has the potential to significantly boost student engagement and improve learning outcomes. 7,8 PBL and CBL are teaching models based on real problems and real cases. 9 Teachers ask questions and provide real cases to stimulate students' learning interest and improve students' clinical ability. 10,11 In this study, AI skin detection system was introduced into the clinical teaching of dermatology cosmetology, and AI was innovatively combined with PBL and CBL joint teaching mode. A prospective randomized controlled trial was conducted to analyze the benefits of integrating AI into cosmetic dermatology education. 2. MATERIALS & METHODS 2.1 Participation and groups According to the inclusion criteria, 70 clinical medical interns were rotated in the Medical Cosmetology Department of the First Affiliated Hospital of South China University from June 2023 to June 2024. All students provided verbal informed consent, and their decision to participate did not influence their grades. As illustrated in Fig. 1 , the participants were randomly assigned to either an experimental group (40 students) or a control group (30 students). Our study was approved by The Ethics Committee of the First Affiliated Hospital of University of South China. All students involved in the study signed informed consent forms. 2.2 Design The course content of the experimental group and the control group was the same, including common pigmented skin diseases in the 4th edition of Cosmetic Dermatology, including melasma, freckles, Ota nevus and hyperpigmentation, etc. taught by the same teacher. The experimental group was taught with AI integrated with PBL and CBL, while the control group was taught with simple PBL combined with CBL. Both groups completed an 8-week rotation in the Department of Medical Cosmetology. The experimental group had classes on Mondays, while the control group attended on Thursdays, with each meeting once per week. Case selection (take acne for example) A deputy chief physician serves as a class teacher and a resident physician serves as an assistant teacher. According to the textbook outline of acne, the head teacher selected 3 typical patients with acne who were treated in the medical cosmetology department as teaching cases. The assistant teacher will communicate with the patient and obtain the patient's consent 3 days before the class starts. After securing patient consent, the assistant shared anonymized information—including medical history, skincare habits, medication history, health status, and facial photos—with students via a newly created WeChat group. The lead instructor prepared diagnostic and treatment-related questions in advance for class discussion. Experimental process (take acne for example) AI combined with PBL and CBL mode: In the experimental group, the assistant teacher helped provide learning materials and teaching equipment. The class started with a brief 10–15 minute lecture where the teacher outlined key points of the disease and introduced relevant cases. The AI skin detection tool was then demonstrated and applied to analyze skin conditions (e.g., porphyrin, pigment, blood vessels, wrinkles, oiliness) (Fig. 2 ). The teacher explained the AI findings, and students were trained on how to use the tool. Next, two patients with acne marks were selected. Students visually analyzed the patients’ skin, summarized case characteristics, and addressed diagnostic and treatment questions posed by the teacher. They then used AI software to analyze the skin, comparing these results with their initial visual assessments, and raised relevant questions. Students consult relevant materials on their own, and teachers guide students to think and discuss problems in practice (such as how AI detection software can accurately classify acne marks, and what inspiration it has for the formulation of final treatment plans). Finally, the students summarized the main points and precautions of AI diagnosis and treatment of acne marks, and the teacher made comments and supplements. Each class lasts about 90 minutes and is held once a week. PBL combined with CBL mode: In the control group, the assistant teacher also provided learning materials and equipment. The teacher began with a 10–15 minute lecture on key disease points and introduced a typical case for analysis. Then, the teacher gave high-definition photos of the faces of the other two patients and asked questions about the differential diagnosis, classification, prevention and treatment of acne marks. Under the guidance of the teacher, the students extracted the key points of knowledge, searched the guide and literature by themselves, and discussed in groups after thinking, and formulated the treatment plan. Finally, the teacher made comments on the students' answers and summarized the key points and difficulties of diagnosis and treatment of acne together with the students. Like the experimental group, the control group's classes were held once a week for 90 minutes. The assistant teacher ensured consistency across both groups by controlling variables such as patient cases, high-definition photos, and the level of difficulty and importance of the knowledge covered. 2.3 Assessment of teaching quality The same assessment was carried out in the experimental group and the control group after 8 weeks of rotation practice, and an anonymous questionnaire survey was conducted at the same time. The exam consists of a theory exam and a case study exam, with a total score of 100. All examination papers and questionnaires are prepared, graded and recorded by the Medical Cosmetology Education Secretary. Theoretical examination The theory exam consists of 5 questions: 1. What are the common pigmented skin diseases and their pathological characteristics? 2. Differential diagnosis, classification and treatment of acne marks? 3. What are the commonly used whitening agents, and how do they work? 4. What are the laser treatments for pigmented diseases, and their indications? 5. What are the latest developments about pigmented skin diseases? The total score is 100, and each question is 20 points. Case analysis There were 2 cases of nevus Ota and melasma. Students are asked to respond in writing to the main points of diagnosis and treatment of the disease associated with the case. The total score is 100 points, 50 points for each case. Questionnaire The questionnaire consists of eight items about students' feelings and opinions about the teaching style, and students fill in "yes" or "no" after each item in the questionnaire. 2.4 Statistical analysis SPSS 27.0 statistical software was used for data input and statistical analysis. Statistical graphics were completed by GraphPad Prism 10.0. The measurement data were expressed as the mean ± standard deviation (‾X ± S). The normal distribution ofthe data was assessed by the Kolmogorov–Smirnov test (K-S test). If the data were normally distributed, the independent samples t-test was used to compare the experimental group and the control group; if the data were not normally distributed, the Mann–Whitney rank-sum test was used. The categorical data were analyzed by Pearson’s chi-square test to compare the difference in gender and the students’ opinions about the teaching methods in two groups. P < 0.05 indicated statistical significance. Table 1 Comparison of general data between two groups of students. Group Sex Age Entrance exam score Male Female Experimental group 15 25 21.23 ± 0.80 80.075 ± 2.390 Control group 13 17 21.30 ± 1.02 80.267 ± 1.856 t value/ χ 2 0.243 0.344 0.364 p value 0.622 0.732 0.717 Table 2 Comparison of test scores between the two groups. Test scores Experimental group n = 40 Control group n = 30 t value p value Theoretical test scores 85.20 ± 2.003 81.90 ± 2.398 6.268 <0.001 Case analysis test scores 80.10 ± 2.520 77.23 ± 1.832 5.270 <0.001 Table 3 Comparison of questionnaire results between the two groups. Items surveyed Experimental group (n = 40) Control group (n = 30) χ 2 p value Yes No Yes No Increase learning interest and motivation 31 9 12 18 10.174 0.0014 Better understanding of diseases and knowledge 29 11 14 16 4.828 0.0280 Improve self-learning ability 27 13 13 17 4.088 0.0432 Cultivate clinical thinking and summary ability 26 14 10 20 6.882 0.0087 Activate the classroom atmosphere 30 10 12 18 8.750 0.0031 Helpful for future clinical work 33 7 11 19 15.425 <0.0001 Occupy time and make a burden 27 13 12 18 5.254 0.0219 Continue to adopt this teaching method 32 8 9 21 17.662 <0.0001 3. RESULTS After completing the eight-week rotation, all 70 participants were assessed. The experimental group consisted of 40 students (14 males, 26 females), while the control group comprised 30 students (11 males, 19 females). There were no statistically significant differences between the two groups in gender, age, and entrance test scores (Table 1 ). The theoretical test and case study results for all participants are displayed in Fig. 3 . No significant differences were observed between the experimental and control groups in overall performance. However, the experimental group scored significantly higher in case analysis compared to the control group (Table 2 ). A total of 70 anonymous questionnaires were collected, with 30 from the control group and 40 from the experimental group. The experimental group outperformed the control group on most teaching effectiveness indicators, including enhanced interest and motivation in learning, better understanding of diseases and knowledge, improved self-directed learning ability, greater clinical thinking and summarizing skills, and a more active classroom environment—all of which were seen as more beneficial for future clinical work (P < 0.05). However, some students believe that ai's combination of PBL and CBL models takes up too much spare time and leads to stress. Overall, the survey indicated that most students in the experimental group preferred the AI-integrated PBL and CBL teaching method (Table 3 ). 4. DISCUSSION Cosmetic skin science is an emerging field of medicine rooted in dermatology and guided by medical aesthetics. It employs dermatological, surgical, physical, and chemical treatment methods to enhance skin health, vitality, and beauty. 12 This discipline encompasses knowledge from human anatomy, dermatology, skincare, skin surgery, and laser cosmetology. In clinical cosmetic dermatology, facial examination is the primary diagnostic method, enabling experienced physicians to rapidly develop treatment plans based on examination results. 13 However, in clinical teaching, students are required to master the clinical manifestations of diseases, differential diagnosis, treatment principles, etc. For many students, these knowledge are difficult to understand, remember and use. For instance, acne, one of the most prevalent skin conditions, must be distinguished from rosacea, folliculitis, and peripilar keratosis. 14 Incorrect treatment can aggravate the condition. Selecting an appropriate treatment plan based on the patient’s skin condition is essential. Identifying whether acne is driven by pigmentation, inflammation, or scarring is the first step in diagnosis and significantly impacts treatment outcomes—a step often challenging for students lacking clinical experience. 15 Treatment options for various types of acne include chemical peels, photo rejuvenation, fractional laser therapy, and oral or topical medications. 16 In the past, treatment outcomes were assessed solely through the doctor's visual judgment. This method often led to discrepancies between the doctor's impressions and the patient's expectations, resulting in inaccurate evaluations of treatment effectiveness and increasing the risk of potential medical disputes. 17 Recently, AI skin detection technology has advanced rapidly, bringing new opportunities to cosmetic dermatology education. 18,19 This study utilizes the Mindscan skin detection system, developed by Wuhan BV Electronics Co., Ltd., China. This multi-spectral skin analysis system, built on Chinese facial feature data and AI technology, employs multi-spectral imaging and high-definition optical lenses for clear, objective skin analysis. The spectral imaging not only reveals surface-level issues like spots, pores, and wrinkles but also uncovers deeper concerns, such as dark spots, blood vessels, and inflammation, using spectrum penetration and AI image analysis. Currently, it is widely used in the Medical Cosmetology Department of our hospital. Traditional teaching methods tend to be teacher-centered, leading to passive knowledge absorption, limited creativity, and reduced student engagement. Combining PBL and CBL methods addresses the limitations of single-method teaching, fostering student participation, enhancing their initiative, and increasing motivation. 20,21 Students gain a deep and comprehensive understanding of knowledge through the iterative process of "practice-understand-re-practice-re-understand." This study integrates AI with PBL and CBL methods to enhance student interest and learning efficiency. The results indicate that students in the experimental group showed significant improvements in both theoretical test scores and case analysis performance. Additionally, the survey revealed a marked increase in classroom satisfaction among these students. Several factors may explain these outcomes: First, students are generally intrigued by new technologies and are eager to use AI software to tackle complex problems. This fosters greater interest in independent learning, shifting them from passive to active learners. Second, the AI skin detection system is user-friendly, has widespread clinical application, and enjoys high acceptance among students, who believe it supports their preparation for future clinical work. 22 Third, the system provides detailed, objective, and quantifiable comparison results, allowing students to clearly differentiate and understand various skin conditions and responses to different treatments. This creates a lasting impression and deepens their comprehension of skin diseases. Finally, the system can store numerous cases and perform quick comparative analyses, freeing up teachers to focus more on refining their pedagogy and increasing student engagement, which enhances the overall teaching and learning process. However, some drawbacks emerged in our study. About 67.5% of students in the experimental group felt that integrating AI into PBL and CBL models increased class duration. We suggest that introducing the AI skin detection system during introductory education would familiarize students with its use, thus saving time in class. It is encouraging that 80% of students in the experimental group still favored combining AI with PBL and CBL teaching methods. That said, our study has some limitations. While both groups received the same course content, slight differences in the delivery of PowerPoint slides could have influenced the results. Additionally, since both groups were taught by the same teacher to avoid teaching bias, double-blinding was not possible, which might have affected the validity of the outcomes. It is worth noting that students in the experimental group are likely to spend more time studying after class, which cannot be accurately accounted for in the experimental design and may lead to a deviation in efficacy. Further randomized controlled trials across multiple teaching hospitals are necessary to substantiate the effectiveness of integrating AI with PBL and CBL models. 5. CONCLUSIONS Overall, integrating AI with PBL and CBL effectively stimulates students' enthusiasm for active learning, enhances their understanding of theoretical and clinical knowledge, and increases teaching satisfaction, making it a valuable approach for broader application. However, this study has some limitations, including a small sample size and its single-center design. The findings need to be validated through larger, multi-center studies. Declarations Funding This study was funded by the Hunan Provincial Teaching Reform Research Project (NO.202401000850) and the Ministry of Education's Industry-University Collaborative Education Project (NO.23100299121500). The funder had no role in the collection of data; the design and conduct of the study; management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Authors’ contributions L. Z. wrote and revised the manuscript. Y. W. created the tables and collected the figures. W. L. provided the conceptual idea and served as a teaching assistant. Y. W. revised the initial tables and figures. X. X. collected the results of the questionnaire. J. L. and C. J. revised the manuscript. N. C. served as a lead teacher, analyze the data and approved the final manuscript. All authors have read and approved the final manuscript. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethical approval and consent to participate All procedures involving human participants in this study complied with the ethical standards of the institutional and/or national research committees, as well as the 1964 Helsinki Declaration and its subsequent amendments, or equivalent ethical guidelines. 1) Informed consent was obtained from all participants, and the study was approved by the Ethics Committee of the University of South China. (NO. 2023LL0525001) 2) All experimental protocols were reviewed and approved by the Ethics Committee/Institutional Review Board of the University of South China. 3) Participation by all students was voluntary, with informed consent obtained from them and/or their legal guardians. Consent for publication All subjects involved in both methods and patients for AI skin detecting in Fig 2 have agreed to participate and be published. Competing interests The authors declare no competing interests. References Martinez-Gonzalez, M. C., Martinez-Gonzalez, R. 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J Med Educ Curric Dev 11 , 23821205241252277 (2024). https://doi.org/10.1177/23821205241252277 Tajerian, A., Kazemian, M., Tajerian, M. & Akhavan Malayeri, A. Design and validation of a new machine-learning-based diagnostic tool for the differentiation of dermatoscopic skin cancer images. PLoS One 18 , e0284437 (2023). https://doi.org/10.1371/journal.pone.0284437 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5307839","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":370523371,"identity":"e6fb5498-9780-498d-a5d2-1664fc59fb37","order_by":0,"name":"Li Zeng","email":"","orcid":"","institution":"The First Affiliated Hospital of the University of South China","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Zeng","suffix":""},{"id":370523372,"identity":"c6f013b8-115e-4a4d-8e4b-3241fb75f2d3","order_by":1,"name":"Yihao Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of the University of South China","correspondingAuthor":false,"prefix":"","firstName":"Yihao","middleName":"","lastName":"Wang","suffix":""},{"id":370523373,"identity":"4741a4df-99df-489d-92b2-b5803b4478e3","order_by":2,"name":"Wanxing Liao","email":"","orcid":"","institution":"University of South China","correspondingAuthor":false,"prefix":"","firstName":"Wanxing","middleName":"","lastName":"Liao","suffix":""},{"id":370523375,"identity":"80cdb123-7671-4bae-8c45-8af4978d3810","order_by":3,"name":"Yiping Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of the University of South China","correspondingAuthor":false,"prefix":"","firstName":"Yiping","middleName":"","lastName":"Wang","suffix":""},{"id":370523376,"identity":"6890c877-fc48-4738-b010-1233ce311e7d","order_by":4,"name":"Xiangping Xu","email":"","orcid":"","institution":"The First Affiliated Hospital of the University of South China","correspondingAuthor":false,"prefix":"","firstName":"Xiangping","middleName":"","lastName":"Xu","suffix":""},{"id":370523379,"identity":"273ee69c-ec32-4dff-b830-e5438a8567de","order_by":5,"name":"Junlin Liao","email":"","orcid":"","institution":"The First Affiliated Hospital of the University of South China","correspondingAuthor":false,"prefix":"","firstName":"Junlin","middleName":"","lastName":"Liao","suffix":""},{"id":370523380,"identity":"e0230f87-9819-47fc-8435-a7fae5d119ec","order_by":6,"name":"Chiyu Jia","email":"","orcid":"","institution":"The First Affiliated Hospital of the University of South China","correspondingAuthor":false,"prefix":"","firstName":"Chiyu","middleName":"","lastName":"Jia","suffix":""},{"id":370523382,"identity":"491d150d-52a8-40b7-aeb7-5c484a8b23a5","order_by":7,"name":"Nian Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYDACCRBhAGV/MLCRI02L5IyCNGMitUCBNM+Hw4kEdfDPbj72uKDALk/evfngbRsD5gQG9sNHN+C15M6xdOMZBsnFhmeOJVvnGLDlMfCkpd3Ap8VAIsdMmseAOXHjDCAjx4CnmEGCx4yAlvxvQC31EC0WBhKJDYS15LABtRxOnA+yjsHAgLAWiRtpIIcdT9zAcyzZsscgwZiNkF/4ZyQ/k+b5U504v7354I0ff/7L8bMfPoZXC8KFB6AMNqKUg4B8A9FKR8EoGAWjYKQBAGFUQ0Ur7xWQAAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of the University of South China","correspondingAuthor":true,"prefix":"","firstName":"Nian","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-10-22 02:53:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5307839/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5307839/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67667130,"identity":"f270b79b-5090-4256-bf72-a724cfa4508d","added_by":"auto","created_at":"2024-10-28 13:46:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":58535,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the whole process of a prospective randomized controlled trial.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5307839/v1/2ef194e26ef6822af3f2fd5d.png"},{"id":67667131,"identity":"ce9aec7d-1a74-48fb-975d-e852042b5170","added_by":"auto","created_at":"2024-10-28 13:46:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":577587,"visible":true,"origin":"","legend":"\u003cp\u003eThe analysis interface of the AI skin detection tool. A: The detection of porphyrin. Porphyrins are metabolites of Propionibacterium acnes, and their quantities can indicate oil secretion and potential acne risk B: The detection of pores. Acne patients often have enlarged pores, and checking the number of enlarged pores can help assess the severity of acne and the effectiveness of treatment C: The detection of pigmentation. Acne patients are often accompanied by post-inflammatory pigmentation, and the software can analyze the area of pigmentation to evaluate the effectiveness of treatment. D: The detection of inflammation. The software can identify subcutaneous inflammation that is difficult to detect with the naked eye, which is helpful for differential diagnosis and treatment planning.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5307839/v1/621dbfb9a2871ff6d670d424.png"},{"id":67667128,"identity":"f2073a26-1140-4ab6-84c3-4577401ef6b7","added_by":"auto","created_at":"2024-10-28 13:46:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":82582,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of test scores between the two groups. (A) Entrance test scores between two groups; (B) Theoretical test scores between two groups; (C) Case analysis test scores between two groups (experimental group: n=40; control group: n= 30).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5307839/v1/c2ef8574e54c661516be361e.png"},{"id":72411650,"identity":"03e1a0e4-1595-4b91-bd6b-f36e43da79e2","added_by":"auto","created_at":"2024-12-26 17:46:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1249478,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5307839/v1/249f2fd6-d737-4639-b7e8-4d3d1ee72d5a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of Teaching Effectiveness of the Combinations of Artificial Intelligence Technology with PBL and CBL in Clinical Dermatology Cosmetology Teaching","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eAs educational models diversify, advanced medical education faces growing challenges in effectively engaging students. Cosmetic dermatology, a branch of medicine focused on skin science and guided by medical aesthetics, studies the function and structure of human skin to maintain, enhance, and promote skin health and beauty.\u003csup\u003e1\u003c/sup\u003e Cosmetic dermatology is a very practical subject, the theoretical knowledge on the book is a summary of the general law of skin science by medical experts, but the symptoms and manifestations of each patient are different. Integrating theoretical knowledge with clinical practice is essential, as treatments must be tailored to each patient\u0026rsquo;s specific condition.\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTraditional lecture-based teaching, where knowledge is passively transmitted from teacher to student, does little to develop students' critical thinking or practical application skills.\u003csup\u003e3\u003c/sup\u003e Additionally, issues like subjective errors, unclear images, and lack of understanding often arise.\u003c/p\u003e \u003cp\u003eRecently, the rapid advancement of AI has led to the increasing use of AI-driven skin detection systems in cosmetic dermatology, offering valuable support for clinical education.\u003csup\u003e4\u003c/sup\u003e Unlike traditional atlas-based teaching, AI can analyze vast amounts of skin image data through machine learning,\u003csup\u003e5\u003c/sup\u003e accurately identifying and quantifying features like pigmentation, wrinkles, erythema, and oiliness, helping to standardize and improve patient consultation. This enhances the precision and efficiency of diagnosing cosmetic skin conditions and guiding treatment.\u003csup\u003e6\u003c/sup\u003e AI technology has the potential to significantly boost student engagement and improve learning outcomes.\u003csup\u003e7,8\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePBL and CBL are teaching models based on real problems and real cases.\u003csup\u003e9\u003c/sup\u003e Teachers ask questions and provide real cases to stimulate students' learning interest and improve students' clinical ability.\u003csup\u003e10,11\u003c/sup\u003e In this study, AI skin detection system was introduced into the clinical teaching of dermatology cosmetology, and AI was innovatively combined with PBL and CBL joint teaching mode. A prospective randomized controlled trial was conducted to analyze the benefits of integrating AI into cosmetic dermatology education.\u003c/p\u003e"},{"header":"2. MATERIALS \u0026 METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participation and groups\u003c/h2\u003e \u003cp\u003eAccording to the inclusion criteria, 70 clinical medical interns were rotated in the Medical Cosmetology Department of the First Affiliated Hospital of South China University from June 2023 to June 2024. All students provided verbal informed consent, and their decision to participate did not influence their grades. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the participants were randomly assigned to either an experimental group (40 students) or a control group (30 students). Our study was approved by The Ethics Committee of the First Affiliated Hospital of University of South China. All students involved in the study signed informed consent forms.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Design\u003c/h2\u003e \u003cp\u003eThe course content of the experimental group and the control group was the same, including common pigmented skin diseases in the 4th edition of Cosmetic Dermatology, including melasma, freckles, Ota nevus and hyperpigmentation, etc. taught by the same teacher. The experimental group was taught with AI integrated with PBL and CBL, while the control group was taught with simple PBL combined with CBL. Both groups completed an 8-week rotation in the Department of Medical Cosmetology. The experimental group had classes on Mondays, while the control group attended on Thursdays, with each meeting once per week.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCase selection (take acne for example)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eA deputy chief physician serves as a class teacher and a resident physician serves as an assistant teacher. According to the textbook outline of acne, the head teacher selected 3 typical patients with acne who were treated in the medical cosmetology department as teaching cases. The assistant teacher will communicate with the patient and obtain the patient's consent 3 days before the class starts. After securing patient consent, the assistant shared anonymized information\u0026mdash;including medical history, skincare habits, medication history, health status, and facial photos\u0026mdash;with students via a newly created WeChat group. The lead instructor prepared diagnostic and treatment-related questions in advance for class discussion.\u003c/p\u003e \u003cp\u003e \u003cem\u003eExperimental process (take acne for example)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAI combined with PBL and CBL mode:\u003c/p\u003e \u003cp\u003eIn the experimental group, the assistant teacher helped provide learning materials and teaching equipment. The class started with a brief 10\u0026ndash;15 minute lecture where the teacher outlined key points of the disease and introduced relevant cases. The AI skin detection tool was then demonstrated and applied to analyze skin conditions (e.g., porphyrin, pigment, blood vessels, wrinkles, oiliness) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The teacher explained the AI findings, and students were trained on how to use the tool. Next, two patients with acne marks were selected. Students visually analyzed the patients\u0026rsquo; skin, summarized case characteristics, and addressed diagnostic and treatment questions posed by the teacher. They then used AI software to analyze the skin, comparing these results with their initial visual assessments, and raised relevant questions. Students consult relevant materials on their own, and teachers guide students to think and discuss problems in practice (such as how AI detection software can accurately classify acne marks, and what inspiration it has for the formulation of final treatment plans). Finally, the students summarized the main points and precautions of AI diagnosis and treatment of acne marks, and the teacher made comments and supplements. Each class lasts about 90 minutes and is held once a week.\u003c/p\u003e \u003cp\u003ePBL combined with CBL mode:\u003c/p\u003e \u003cp\u003eIn the control group, the assistant teacher also provided learning materials and equipment. The teacher began with a 10\u0026ndash;15 minute lecture on key disease points and introduced a typical case for analysis. Then, the teacher gave high-definition photos of the faces of the other two patients and asked questions about the differential diagnosis, classification, prevention and treatment of acne marks. Under the guidance of the teacher, the students extracted the key points of knowledge, searched the guide and literature by themselves, and discussed in groups after thinking, and formulated the treatment plan. Finally, the teacher made comments on the students' answers and summarized the key points and difficulties of diagnosis and treatment of acne together with the students. Like the experimental group, the control group's classes were held once a week for 90 minutes. The assistant teacher ensured consistency across both groups by controlling variables such as patient cases, high-definition photos, and the level of difficulty and importance of the knowledge covered.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Assessment of teaching quality\u003c/h2\u003e \u003cp\u003eThe same assessment was carried out in the experimental group and the control group after 8 weeks of rotation practice, and an anonymous questionnaire survey was conducted at the same time. The exam consists of a theory exam and a case study exam, with a total score of 100. All examination papers and questionnaires are prepared, graded and recorded by the Medical Cosmetology Education Secretary.\u003c/p\u003e \u003cp\u003e \u003cem\u003eTheoretical examination\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe theory exam consists of 5 questions: 1. What are the common pigmented skin diseases and their pathological characteristics? 2. Differential diagnosis, classification and treatment of acne marks? 3. What are the commonly used whitening agents, and how do they work? 4. What are the laser treatments for pigmented diseases, and their indications? 5. What are the latest developments about pigmented skin diseases? The total score is 100, and each question is 20 points.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCase analysis\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThere were 2 cases of nevus Ota and melasma. Students are asked to respond in writing to the main points of diagnosis and treatment of the disease associated with the case. The total score is 100 points, 50 points for each case.\u003c/p\u003e \u003cp\u003e \u003cem\u003eQuestionnaire\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe questionnaire consists of eight items about students' feelings and opinions about the teaching style, and students fill in \"yes\" or \"no\" after each item in the questionnaire.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eSPSS 27.0 statistical software was used for data input and statistical analysis. Statistical graphics were completed by GraphPad Prism 10.0. The measurement data were expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (\u0026oline;X\u0026thinsp;\u0026plusmn;\u0026thinsp;S). The normal distribution ofthe data was assessed by the Kolmogorov\u0026ndash;Smirnov test (K-S test). If the data were normally distributed, the independent samples t-test was used to compare the experimental group and the control group; if the data were not normally distributed, the Mann\u0026ndash;Whitney rank-sum test was used. The categorical data were analyzed by Pearson\u0026rsquo;s chi-square test to compare the difference in gender and the students\u0026rsquo; opinions about the teaching methods in two groups. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated statistical significance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of general data between two groups of students.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEntrance exam score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperimental group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80.075\u0026thinsp;\u0026plusmn;\u0026thinsp;2.390\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80.267\u0026thinsp;\u0026plusmn;\u0026thinsp;1.856\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e value/\u003cem\u003eχ\u003c/em\u003e \u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of test scores between the two groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest scores\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental group\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;40\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl group\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;30\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheoretical test scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.20\u0026thinsp;\u0026plusmn;\u0026thinsp;2.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.90\u0026thinsp;\u0026plusmn;\u0026thinsp;2.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase analysis test scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.10\u0026thinsp;\u0026plusmn;\u0026thinsp;2.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of questionnaire results between the two groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eItems surveyed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eExperimental group (n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eControl group (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e \u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncrease learning interest and motivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBetter understanding of diseases and knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0280\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImprove self-learning ability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCultivate clinical thinking and summary ability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivate the classroom atmosphere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHelpful for future clinical work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupy time and make a burden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0219\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinue to adopt this teaching method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003eAfter completing the eight-week rotation, all 70 participants were assessed. The experimental group consisted of 40 students (14 males, 26 females), while the control group comprised 30 students (11 males, 19 females). There were no statistically significant differences between the two groups in gender, age, and entrance test scores (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The theoretical test and case study results for all participants are displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. No significant differences were observed between the experimental and control groups in overall performance. However, the experimental group scored significantly higher in case analysis compared to the control group (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A total of 70 anonymous questionnaires were collected, with 30 from the control group and 40 from the experimental group. The experimental group outperformed the control group on most teaching effectiveness indicators, including enhanced interest and motivation in learning, better understanding of diseases and knowledge, improved self-directed learning ability, greater clinical thinking and summarizing skills, and a more active classroom environment\u0026mdash;all of which were seen as more beneficial for future clinical work (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, some students believe that ai's combination of PBL and CBL models takes up too much spare time and leads to stress. Overall, the survey indicated that most students in the experimental group preferred the AI-integrated PBL and CBL teaching method (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eCosmetic skin science is an emerging field of medicine rooted in dermatology and guided by medical aesthetics. It employs dermatological, surgical, physical, and chemical treatment methods to enhance skin health, vitality, and beauty.\u003csup\u003e12\u003c/sup\u003e This discipline encompasses knowledge from human anatomy, dermatology, skincare, skin surgery, and laser cosmetology.\u003c/p\u003e \u003cp\u003eIn clinical cosmetic dermatology, facial examination is the primary diagnostic method, enabling experienced physicians to rapidly develop treatment plans based on examination results.\u003csup\u003e13\u003c/sup\u003e However, in clinical teaching, students are required to master the clinical manifestations of diseases, differential diagnosis, treatment principles, etc. For many students, these knowledge are difficult to understand, remember and use. For instance, acne, one of the most prevalent skin conditions, must be distinguished from rosacea, folliculitis, and peripilar keratosis.\u003csup\u003e14\u003c/sup\u003e Incorrect treatment can aggravate the condition. Selecting an appropriate treatment plan based on the patient\u0026rsquo;s skin condition is essential. Identifying whether acne is driven by pigmentation, inflammation, or scarring is the first step in diagnosis and significantly impacts treatment outcomes\u0026mdash;a step often challenging for students lacking clinical experience.\u003csup\u003e15\u003c/sup\u003e Treatment options for various types of acne include chemical peels, photo rejuvenation, fractional laser therapy, and oral or topical medications.\u003csup\u003e16\u003c/sup\u003e In the past, treatment outcomes were assessed solely through the doctor's visual judgment. This method often led to discrepancies between the doctor's impressions and the patient's expectations, resulting in inaccurate evaluations of treatment effectiveness and increasing the risk of potential medical disputes.\u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eRecently, AI skin detection technology has advanced rapidly, bringing new opportunities to cosmetic dermatology education.\u003csup\u003e18,19\u003c/sup\u003e This study utilizes the Mindscan skin detection system, developed by Wuhan BV Electronics Co., Ltd., China. This multi-spectral skin analysis system, built on Chinese facial feature data and AI technology, employs multi-spectral imaging and high-definition optical lenses for clear, objective skin analysis. The spectral imaging not only reveals surface-level issues like spots, pores, and wrinkles but also uncovers deeper concerns, such as dark spots, blood vessels, and inflammation, using spectrum penetration and AI image analysis. Currently, it is widely used in the Medical Cosmetology Department of our hospital.\u003c/p\u003e \u003cp\u003eTraditional teaching methods tend to be teacher-centered, leading to passive knowledge absorption, limited creativity, and reduced student engagement. Combining PBL and CBL methods addresses the limitations of single-method teaching, fostering student participation, enhancing their initiative, and increasing motivation.\u003csup\u003e20,21\u003c/sup\u003e Students gain a deep and comprehensive understanding of knowledge through the iterative process of \"practice-understand-re-practice-re-understand.\" This study integrates AI with PBL and CBL methods to enhance student interest and learning efficiency.\u003c/p\u003e \u003cp\u003eThe results indicate that students in the experimental group showed significant improvements in both theoretical test scores and case analysis performance. Additionally, the survey revealed a marked increase in classroom satisfaction among these students. Several factors may explain these outcomes: First, students are generally intrigued by new technologies and are eager to use AI software to tackle complex problems. This fosters greater interest in independent learning, shifting them from passive to active learners. Second, the AI skin detection system is user-friendly, has widespread clinical application, and enjoys high acceptance among students, who believe it supports their preparation for future clinical work.\u003csup\u003e22\u003c/sup\u003e Third, the system provides detailed, objective, and quantifiable comparison results, allowing students to clearly differentiate and understand various skin conditions and responses to different treatments. This creates a lasting impression and deepens their comprehension of skin diseases. Finally, the system can store numerous cases and perform quick comparative analyses, freeing up teachers to focus more on refining their pedagogy and increasing student engagement, which enhances the overall teaching and learning process.\u003c/p\u003e \u003cp\u003eHowever, some drawbacks emerged in our study. About 67.5% of students in the experimental group felt that integrating AI into PBL and CBL models increased class duration. We suggest that introducing the AI skin detection system during introductory education would familiarize students with its use, thus saving time in class. It is encouraging that 80% of students in the experimental group still favored combining AI with PBL and CBL teaching methods.\u003c/p\u003e \u003cp\u003eThat said, our study has some limitations. While both groups received the same course content, slight differences in the delivery of PowerPoint slides could have influenced the results. Additionally, since both groups were taught by the same teacher to avoid teaching bias, double-blinding was not possible, which might have affected the validity of the outcomes. It is worth noting that students in the experimental group are likely to spend more time studying after class, which cannot be accurately accounted for in the experimental design and may lead to a deviation in efficacy. Further randomized controlled trials across multiple teaching hospitals are necessary to substantiate the effectiveness of integrating AI with PBL and CBL models.\u003c/p\u003e"},{"header":"5. CONCLUSIONS","content":"\u003cp\u003eOverall, integrating AI with PBL and CBL effectively stimulates students' enthusiasm for active learning, enhances their understanding of theoretical and clinical knowledge, and increases teaching satisfaction, making it a valuable approach for broader application. However, this study has some limitations, including a small sample size and its single-center design. The findings need to be validated through larger, multi-center studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Hunan Provincial Teaching Reform Research Project (NO.202401000850) and the Ministry of Education\u0026apos;s Industry-University Collaborative Education Project (NO.23100299121500).\u0026nbsp;The funder had no role in the collection of data; the design and conduct of the study; management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eL. Z. wrote and revised the manuscript. Y. W. created the tables and collected the figures. W. L. provided the conceptual idea and served as a teaching assistant. Y. W. revised the initial tables and figures. X. X. collected the results of the questionnaire. J. L. and C. J. revised the manuscript. N. C. served as a lead teacher, analyze the data and approved the final manuscript. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures involving human participants in this study complied with the ethical standards of the institutional and/or national research committees, as well as the 1964 Helsinki Declaration and its subsequent amendments, or equivalent ethical guidelines.\u003c/p\u003e\n\u003cp\u003e1) Informed consent was obtained from all participants, and the study was approved by the Ethics Committee of the University of South China. (NO. 2023LL0525001)\u003c/p\u003e\n\u003cp\u003e2) All experimental protocols were reviewed and approved by the Ethics Committee/Institutional Review Board of the University of South China.\u003c/p\u003e\n\u003cp\u003e3) Participation by all students was voluntary, with informed consent obtained from them and/or their legal guardians.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll subjects involved in both methods and patients for AI skin detecting in Fig 2 have agreed to participate and be published.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMartinez-Gonzalez, M. C., Martinez-Gonzalez, R. A. \u0026amp; Guerra-Tapia, A. 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Design and validation of a new machine-learning-based diagnostic tool for the differentiation of dermatoscopic skin cancer images. \u003cem\u003ePLoS One\u003c/em\u003e\u003cstrong\u003e18\u003c/strong\u003e, e0284437 (2023). https://doi.org/10.1371/journal.pone.0284437\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5307839/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5307839/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThis study aimed to evaluate the effectiveness of integrating artificial intelligence (AI) with problem-based learning (PBL) and case-based learning (CBL) in a dermatology cosmetology course for clinical medical interns.\u003c/p\u003e\u003ch2\u003eMaterials \u0026amp; Methods\u003c/h2\u003e \u003cp\u003eThis prospective randomized controlled trial involved 70 clinical interns rotating in the medical cosmetology department. Participants were randomly assigned to two groups and followed the same curriculum over 8 weeks. The experimental group (n\u0026thinsp;=\u0026thinsp;40) used a teaching method combining AI with PBL and CBL, while the control group (n\u0026thinsp;=\u0026thinsp;30) followed the traditional PBL and CBL approach. Assessments included theoretical exams, case analysis, and anonymous feedback surveys to evaluate teaching quality.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAll participants completed the examinations and questionnaires. The average theoretical test scores and case analysis test scores of the experimental group were higher than those of the control group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The indicators of the experimental group\u0026rsquo;s feedback were better than those of the control group, such that the improvement of learning interest and motivation, the improvement of understanding of diseases and knowledge, the improvement of independent learning ability, the improvement of clinical thinking and summary ability, as well as a more positive classroom atmosphere and more helpful for future clinical work (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCompared to the PBL combined CBL teaching method, the teaching mode combining AI with PBL and CBL teaching method showed a higher efficacy. The learning model effectively improved students\u0026rsquo; outcomes and satisfaction, which helped students narrow the gap between theoretical knowledge and clinical practical application.\u003c/p\u003e","manuscriptTitle":"Analysis of Teaching Effectiveness of the Combinations of Artificial Intelligence Technology with PBL and CBL in Clinical Dermatology Cosmetology Teaching","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-28 13:46:23","doi":"10.21203/rs.3.rs-5307839/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4bba7050-3e37-481f-bffa-6816c8587d1e","owner":[],"postedDate":"October 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-26T17:38:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-28 13:46:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5307839","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5307839","identity":"rs-5307839","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}