Using ChatGPT for Writing Articles for Patients' Education for Dermatological Diseases: A Pilot Study.

BackgroundThe acquisition of in-depth medical knowledge, skills, and competencies is of utmost importance when training future medical professionals. This systematic literature review delves into the empirical connection between gamified learning in medical education and the Structure of Observed Learning Outcomes (SOLO) taxonomy.MethodsFollowing PRISMA guidelines, a systematic literature review was conducted in seven databases to identify empirical studies related to gamification and medical education. The literature search was limited to peer-reviewed articles published between January 2012 and December 2022. Articles focusing on games or learning technologies in a broader sense as well as research focusing on nursing or pharmacy education were excluded.ResultsUpon reviewing 23 qualified empirical studies that applied gamified learning strategies in medical education, 18 (78%) studies are associated with the second lowest level (uni-structural) of the SOLO taxonomy. The mid-level (multi-structural) learning outcomes are associated with three (13%) of the reviewed studies. There are five (22%) studies focusing on the second highest (relational) level of the SOLO. Only one study (4%) is associated with the highest SOLO level (extended-abstract). Finally, three (13%) studies were identified with multiple levels of the SOLO. In addition to the SOLO levels, the review found six (26%) studies emphasizing motivational and engagement support of gamified learning strategies in facilitating intended learning outcome attainment. A total of three (13%) studies, across three SOLO levels, suggested that gamified learning strategies can improve students’ soft skills in medical education programs.ConclusionThese findings collectively emphasize the need for future research and development to design gamified learning experiences capable of fostering higher SOLO taxonomy attainment in medical education. Moreover, there is potential to extend the SOLO framework to encompass motivational and affective learning outcomes, providing a comprehensive understanding of the impact of gamified learning on medical students.

Assessment of student learning in complex areas is challenging, particularly when there is interest in students’ deeper understanding and connectivity of concepts. Assessment of ethics learning has been limited by lack of consensus regarding what is effective and an overfocus on quantification at the expense of clinical or ethical relevance. Cognitive maps provide one means to evaluate depth and personal meaning of students’ ethics knowledge. A pre-/post-test design using cognitive maps and the structure of observed learning outcome (SOLO) taxonomy was used to assess student learning on completion of a six-week intensive ethics course. Thirty-seven students completed the research. Student learning was supported by significant changes in component (content, labelling, relationships and structure/visual message), and total pre- and post-test cognitive map scores, and significant change in pre- and post-test SOLO scores. A strong relationship was found between cognitive map improvement and SOLO improvement. Student written commentaries describing their post-test cognitive map provided a richer, more elaborated account of their understanding that qualitatively enhanced their cognitive maps. Cognitive maps offer an alternative to traditional ethics assessment strategies.

Automata for knowledge assessment based on the structure of observed learning outcome taxonomy

Assessing student learning outcomes has been an integral and central component of schools and academic institutions, both as a measure of how well the school is performing and how well individual students are progressing. However, standardised tests might not be able to fully capture holistic aspects of learning such as self-regulatory competencies, motivation and interest. The Structure of Observed Learning Outcomes (SOLO) taxonomy has been used to examine the quality of learning outcomes. Based on findings from the current study in a STEM-based learning curriculum, a new model, the Structure of Observed Learning and Interest Outcomes (SOLIO) taxonomy, is proposed to capture student interest development as well as learning outcomes for a more holistic assessment of learning.

The research compared and assessed 6th grade student large-scale assessment item indicators according to the revised competency-based Science curriculum in Latvia, in order to understand what improvements are needed in the large-scale assessment for aligning the national assessments with the new national curriculum, and thus ensuring successful implementation of the educational reform. The theoretical framework of the research was developed by using the frameworks of the Programme for International Student Assessment (PISA), and cognitive level was measured according to the Structure of Observed Learning Outcomes (SOLO) taxonomy. Keywords: assessment alignment to curriculum, educational reform, scientific literacy.

Online learning imposed by government as an impact of the COVID-19 pandemic affected students’ ability to understand the authentic problem. Authentic problem used to train students’ ability to apply their knowledge in physics. This research mainly discuss es about the students' levels understanding of the authentic problem that is SARS-CoV-2 airborne transmission using the SOLO taxonomy. This research used a descriptive qualitative approach using eight question s related to SARS-CoV-2 airborne transmission. Data collected via online survey platform and analyzed by calculating the percentage of student's level of understanding for each given question. The result shown that 59% student included in pre-structural level, 19% included in uni-structural level, 10% included in multi-structural level, 3% included in relational level, and 10% included in extended-abstract level.

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contribution:</i> Insights are provided into the intended role of integrative courses that aim to connect technical and professional capabilities and, particularly, the appropriate stage within a program for different levels of integrative capability. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background:</i> The need for Engineering graduates who can balance technical competence with professional and transversal capabilities is well recognized. Integrative multidisciplinary projects have become an increasingly common approach to managing this balance and have been the subject of significant research. There has, however, been minimal consideration of the level of integrative skills that might be considered as appropriate at different stages of degree programs. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Research Questions:</i> Do intended learning outcomes of integrative projects vary with their stage in a degree programs? If so, then what does this variation reveal regarding expectations on the development of integrative capabilities? <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methodology:</i> Examples of project units that claim to be addressing integrative objectives and for which learning outcomes are available were collected ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N=33$ </tex-math></inline-formula> ). Guided by the existing literature on the purpose of integrative units a thematic analysis of these units was undertaken by coding the learning outcomes against the structure of observed learning outcome (SOLO) taxonomy. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Findings:</i> Within the existing units, there is clear evidence, as projects move from junior to senior, of a progression in the breadth of knowledge that is being drawn on in carrying out the projects, and the nature of knowledge integration becomes more purposeful. There is, however, no evidence of an associated progression in the intended level of integrative capability that is expected (e.g., identifying richer connections). This suggests the critical need for more work in this area.

Introduction Obesity, hypertension, and hypertriglyceridemia are key components of metabolic syndrome, a major contributor to cardiovascular diseases (CVDs), which remain a leading cause of global mortality. Patient education on these conditions can empower individuals to adopt preventive measures and manage risks effectively. This study compares ChatGPT and Google Gemini, two prominent artificial intelligence (AI) tools, to evaluate their utility in creating patient education guides. ChatGPT is known for its conversational depth, while Google Gemini emphasizes advanced natural language processing. By analyzing readability, reliability, and content characteristics, the study highlights how these AI tools cater to diverse patient needs, aiming to enhance health literacy outcomes. Methodology A cross-sectional study evaluated patient education guides on obesity, hypertension, and hypertriglyceridemia, focusing on their links to metabolic syndrome. Responses from ChatGPT and Google Gemini were analyzed for word count, sentence count, readability (using the Flesch-Kincaid calculator), similarity score (using Quillbot), and reliability score (using the modified DISCERN score), with statistical analyses performed using the R Version 4.3.2. Results Statistical analysis revealed a significant differencein word and sentence counts between the AI tools: ChatGPT averaged 591.50 words and 66 sentences, while Google Gemini had 351.50 words and 36 sentences (p = 0.001 andp < 0.0001). However, the average words per sentence, average syllables per word, grade level, similarity percentage, and reliability scores did not differ significantly. Although Google Gemini had a higher ease score (41.75) compared to ChatGPT (34.10), this difference was not statistically significant (p = 0.080). Both tools exhibited similar readability and reliability, indicating their effectiveness for patient education, despite ChatGPT providing longer responses. Conclusion The study found no significant difference between the two AI tools in terms of ease, grade, and reliability scores, with no correlation between ease and reliability scores.

Descriptive study. To explore the role of artificial intelligence (AI) in disseminating information and addressing frequently asked questions related to spinal cord injuries (SCIs) and associated complications. Advancements in AI have revolutionized patient education, particularly in the context of traumatic injuries. Spine trauma, owing to its potential to significantly alter the course of patients' lives, causes many concerns related to symptomatology, potential interventions, and recovery processes. We utilized 4 commonly used AI models (ChatGPT, Bing AI, Google SGE, and Perplexity AI) to address common questions posed by individuals dealing with spine trauma. Responses were evaluated using the DISCERN instrument for information quality and readability indices for reading levels. DISCERN scores highlighted variability in information quality across the AI models. Bing AI demonstrated reliability in specific categories, while ChatGPT exhibited balanced scores, showcasing versatility. Google SGE presented room for improvement, indicated by lower scores in various categories. Perplexity AI consistently provided high-quality information across all evaluated domains. Variability in AI performance was largely influenced by differences in training data and inference methods. A comprehensive approach that leverages the strengths of multiple models may enhance overall information dissemination, and addressing the limitations of lower-scoring models could improve AI's potential as an educational tool for spine trauma. AI has a pivotal role in reshaping patient education for individuals grappling with spinal cord injuries. Our findings provide insights into the strengths and areas for improvement across different AI models, highlighting the need for a more informed and nuanced approach by health care providers leveraging AI in patient education. Future research should investigate AI integration into patient education across various conditions, focusing on clinical accuracy, relevance, and resource considerations.

Breast cancer remains a global public health burden. This study aimed to evaluate the readability of breast cancer articles shared on X (formerly Twitter) during Breast Cancer Awareness Month (October 2024), and it explores the possibility of using artificial intelligence (AI) to improve readability. We identified the top articles (n = 377) from posts containing #breastcancer on X during October 2024. Each article was analyzed using 9 established readability tests: Automated Readability Index (ARI), Coleman-Liau, Flesch-Kincaid, Flesch Reading Ease, FORCAST Readability Formula, Fry Graph, Gunning Fog Index, Raygor Readability Estimate, and Simple Measure of Gobbledygook (SMOG) Readability Formula. The study categorized sharing entities into five groups: academic medical centers, healthcare providers, government institutions, scientific journals, and all others. This comprehensive approach aimed to evaluate the readability of breast cancer articles across various sources during a critical awareness period of peak public engagement. A pilot study was simultaneously conducted using AI to improve readability. Statistical analysis was performed using SPSS. A total of 377 articles shared by the following entities were analyzed: academic medical centers (35, 9.3%), healthcare providers (57, 15.2%), government institutions (21, 5.6%), scientific journals (63, 16.8%), and all others (199, 53.1%). Government institutions shared articles with the lowest average readability grade level (12.71 ± 0.79). Scientific journals (16.57 ± 0.09), healthcare providers (15.49 ± 0.32), all others (14.89 ± 0.17), and academic medical centers (13.56 ± 0.39) had higher average readability grade levels. Article types were also split into different categories: patient education (222, 58.9%), open-access journal (119, 31.5%), and full journal (37, 9.6%). Patient education articles (15.19 ± 0.17) had the lowest average readability grade level. Open-access and full journals had an average readability grade level of 16.65 ± 0.06 and 16.53 ± 0.10, respectively. The mean values for Flesch Reading Ease Score are patient education 38.14 ± 1.2, open-access journals 16.14 ± 0.89, full journals 17.69 ± 2.14. Of note, lower readability grade levels indicate easier-to-read text, while higher Flesch Reading Ease scores indicate more ease of reading. In a demonstration using AI to improve readability grade level of 5 sample articles, AI successfully lowered the average readability grade level from 12.58 ± 0.83 to 6.56 ± 0.28 (p < 0.001). Our findings highlight a critical gap between the recommended and actual readability levels of breast cancer information shared on a popular social media platform. While some institutions are producing more accessible content, there is a pressing need for standardization and improvement across all sources. To address this issue, sources may consider leveraging AI technology as a potential tool for creating patient resources with appropriate readability levels.

The use of tunnel boring machine (TBM) in mechanized tunnelling excavation in various ground conditions has been highlighted in many projects. In these projects, estimation of the TBM performance is considered as a significant issue since it can be an influential parameter related to the project cost. Hence, many scholars tried to develop simple, applicable, and powerful methodologies for the prediction of TBM performance. The total developed methods in this regard can be divided into four categories, namely, theoretical, empirical, statistical, and computational. In this study, the advantages and disadvantages of these techniques were discussed. Many investigators mentioned that empirical and theoretical techniques are not good enough in accurate prediction of TBM performance. Some other researchers developed statistical-based models/equations in predicting TBM performance. However, their accuracy level is only suitable (coefficient of determination ~0.6) in many cases. On the other hand, these techniques are not good if there are some outlier data samples in the database. The best model category for TBM performance prediction is related to machine learning (ML) and artificial intelligence (AI) techniques. Using these techniques, a complex problem (i.e., TBM performance) can be solved with a high level of accuracy and low level of system error (coefficient of determination ~0.9). This study concluded that ML and AI are considered as accurate, powerful, and simple techniques in the area of tunnelling and they can be used in other applications of geotechnics as well.KeywordsTunnel boring machineTBM performanceTheoretical and empirical techniquesMachine learningStatistical-Based techniques

Artificial Intelligence (AI) has emerged as one of the most transformative technological developments of the modern era, offering a profound impact on various sectors, including education. The integration of AI into education presents a complex interplay of opportunities, challenges, and risks that require comprehensive exploration. This study adopts a mixed-methods approach, utilizing bibliometric, conceptual, and hermeneutic analyses to examine the role of AI in educational contexts. Bibliometric analysis quantitatively and visually maps research trends, employing tools to analyze 8,288 documents retrieved from the Scopus database using the keywords "Artificial Intelligence" OR "AI" AND "Education." Over the past two decades, the field has seen exponential growth, with contributions from 28,740 authors worldwide. The mapping highlights key areas of AI application, such as machine learning, deep learning, educational engineering, and computer-aided instruction, alongside technologies like ChatGPT, virtual reality, big data, and data mining. Conceptual analysis dissects critical ideas such as posthumanism, transhumanism, and their intersection with AI in education. These paradigms are reshaping education by challenging its traditional roles as a vehicle for human development and societal progress. AI's role in facilitating personalized learning experiences underscores its potential for enhancing efficiency and adaptability. However, it also raises pressing questions about its alignment with broader educational objectives. Beyond knowledge transfer, education fosters character development, critical thinking, and social skills areas potentially undermined by overreliance on AI technologies. The hermeneutic method provides a deeper interpretation of philosophical texts and literature, emphasizing the ethical and societal implications of integrating AI into education. Posthumanism and transhumanism, which advocate for merging human capabilities with technological advancements, pose existential challenges to education's foundational values. Tools like ChatGPT, while powerful, risk eroding academic integrity and critical skill development. For instance, reliance on AI for solving exam problems or answering complex queries undermines the principles of effort and intellectual growth, essential components of education. The research underscores the dual-edged nature of AI in education. On one hand, it enables adaptive and efficient learning systems. On the other hand, it disrupts traditional educational practices, raising ethical dilemmas and questions about the future of human-centric learning. Philosophical perspectives are crucial in navigating these disruptions, advocating for an education system that balances technological integration with the preservation of core academic values. This involves addressing the ethical considerations surrounding AI applications, particularly the potential misuse of tools like ChatGPT, which are byproducts of posthumanist and transhumanist ideologies. Education, as a transformative process, must evolve to integrate AI responsibly, ensuring that it complements rather than replaces human intellectual efforts. This requires reconstructing educational systems to mitigate the unintended consequences of AI technologies. Philosophical inquiry into the purpose of education must guide the realignment of its goals, emphasizing how knowledge is acquired and skills are nurtured in a rapidly evolving digital landscape. By fostering awareness of the ethical and practical implications of AI, educational institutions can reclaim their role in cultivating well-rounded individuals capable of critical thought and ethical decision-making. In conclusion, this study highlights the need for a balanced approach to integrating AI into education. While technological advancements offer unprecedented opportunities for innovation, they must not overshadow the humanistic values at the core of education. By addressing the challenges posed by posthumanism and transhumanism, the educational sector can harness AI's potential while safeguarding its mission to develop responsible, ethical, and capable individuals for the 21st century.

The incorporation of Artificial Intelligence (AI) into healthcare sector has fundamentally transformed patient care paradigms, particularly through the creation of patient education materials (PEMs) tailored to individual needs. This Study aims to assess the precision and readability AI-generated information on kidney cancer using ChatGPT 4.0, Gemini AI, and Perplexity AI., comparing these outputs to PEMs provided by the American Urological Association (AUA) and the European Association of Urology (EAU). The objective is to guide physicians in directing patients to accurate and understandable resources. PEMs published by AUA and EAU were collected and categorized. kidney cancer-related queries, identified via Google Trends (GT), were input into CahtGPT-4.0, Gemini AI, and Perplexity AI. Four independent reviewers assessed the AI outputs for accuracy grounded on five distinct categories, employing a 5-point Likert scale. A readability evaluation was conducted utilizing established formulas, including Gunning Fog Index (GFI), Simple Measure of Gobbledygook (SMOG), and Flesch-Kincaid Grade Formula (FKGL). AI chatbots were then tasked with simplifying their outputs to achieve a sixth-grade reading level. The PEM published by the AUA was the most readable with a mean readability score of 9.84 ± 1.2, in contrast to EAU (11.88 ± 1.11), ChatGPT-4.0 (11.03 ± 1.76), Perplexity AI (12.66 ± 1.83), and Gemini AI (10.83 ± 2.31). The Chatbots demonstrated the capability to simplify text lower grade levels upon request, with ChatGPT-4.0 achieving a readability grade level ranging from 5.76 to 9.19, Perplexity AI from 7.33 to 8.45, Gemini AI from 6.43 to 8.43. While official PEMS were considered accurate, the LLMs generated outputs exhibited an overall high level of accuracy with minor detail omission and some information inaccuracies. Information related to kidney cancer treatment was found to be the least accurate among the evaluated categories. Although the PEM published by AUA being the most readable, both authoritative PEMs and Large Language Models (LLMs) generated outputs exceeded the recommended readability threshold for general population. AI Chatbots can simplify their outputs when explicitly instructed. However, notwithstanding their accuracy, LLMs-generated outputs are susceptible to detail omission and inaccuracies. The variability in AI performance necessitates cautious use as an adjunctive tool in patient education.

Artificial Intelligence (AI) is reshaping preoperative preparation for both patients and surgeons in plastic surgery, offering innovative tools that improve efficiency, precision, and outcomes. This manuscript examines the growing role of AI in preoperative patient education, surgeon preparation, and clinical workflows, and provides an overview of its main domains of impact. AI-powered tools show great promise in consultations, providing personalized, clear patient education, simplifying administrative tasks such as drafting consultation letters, and enhancing communication. For surgeons, AI can support preoperative planning with reinforcement learning, advanced imaging algorithms, and virtual simulations. These technologies assist in accurate surgical decision making, as seen in deep inferior epigastric artery perforator (DIEP) flap breast reconstruction, where AI may be able to reduce perforator analysis times while maintaining precision. In aesthetic surgery, AI-driven 3D imaging and generative algorithms enable realistic postoperative simulations, improving communication and helping patients set realistic expectations. From a patient communication perspective, AI promotes patient-centered care by simplifying medical jargon, making information more accessible, and fostering better understanding and adherence. Despite challenges such as ethical considerations, data security, and algorithmic bias, AI’s integration into preoperative workflows highlights its potential to improve patient and surgeon experiences. By enhancing patient education, refining surgical strategies, and optimizing preparation, AI is redefining how care is delivered in plastic surgery, aiming for better patient satisfaction and outcomes. Collaboration among clinicians, engineers, and ethicists remains crucial to ensure the responsible use of AI, balancing innovation with the core values of medical care.

Integrating artificial intelligence (AI) in cosmetic surgery presents both transformative opportunities and significant challenges. From preoperative planning to postoperative care, AI offers tools that enhance precision, efficiency, and personalization. However, concerns such as algorithmic bias, ethical implications, and the potential depersonalization of patient care must be carefully considered. This article explores AI’s impact on cosmetic surgery, addressing its benefits, risks, and ethical considerations while proposing solutions on evidence-based strategies for safe and effective AI integration into clinical practice. By evaluating AI-driven advancements in patient education, treatment planning, and postoperative management, we aim to equip surgeons with actionable insights for navigating the future of our field. To fully realize AI’s potential in aesthetic and cosmetic surgery, it is essential to approach its integration through a structured framework that balances innovation with responsibility. This includes identifying key risk areas, implementing mitigation strategies, and outlining best practices for clinical adoption. The following sections are organized around major challenges—such as bias, ethical concerns, and depersonalization—and we aim to offer specific solutions to guide surgeons in applying AI thoughtfully and safely within their own practices. A combination of keywords related to “cosmetic surgery” and “artificial intelligence,” such as “AI trends,” “AI risks,” and “AI in surgery,” was used for a literature search across PubMed and Web of Science. The search was focused on published articles in English, in the past 10 years, to point out the recent trends and risks arising due to the use of AI in cosmetic plastic surgery. The search showed a long list of potential risks and complications associated with the use of AI when not applied carefully. On the contrary, the search also showed its versatility and colossal potential in transforming the field of cosmetic plastic surgery. While AI offers promising advancements in cosmetic plastic surgery, it is essential to navigate its integration carefully. By addressing issues such as potential biases, ethical concerns, and the risk of depersonalization, the field can leverage AI to enhance patient care and outcomes. The future of facial cosmetic plastic surgery with AI holds great potential, but it must be grounded in principles that prioritize patient safety, inclusivity, and the preservation of the human touch.