• MeSH
• Cognition MeSH
• Communication MeSH
• Ethics, Medical MeSH
• Humans MeSH
• Models, Theoretical MeSH
• Artificial Intelligence MeSH
• Medical Informatics Applications * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Editorial MeSH

Tento přehledový článek se zaměřuje na základní principy technologií umělé inteligence (AI), možnosti jejich využití v medicíně a na příklady aplikací, které již byly začleněny do klinické praxe. Diskutuje také klíčové výzvy včetně etických otázek, jako je ochrana soukromí pacientů, algoritmická bias a problém transparentnosti modelů AI. Článek zdůrazňuje nutnost integrace AI do medicíny způsobem, který zajistí bezpečnost a důvěryhodnost, a současně vyzdvihuje význam vzdělávání zdravotnických profesionálů v oblasti AI. Umělá inteligence nabízí potenciál ke zlepšení přesnosti diagnostiky, efektivity péče a podpory při klinickém rozhodování, přičemž optimálních výsledků lze dosáhnout spoluprací mezi lékaři a systémy AI.

This review article focuses on the fundamental principles of artificial intelligence (AI) technologies, their utilisation in medicine, and examples of applications that have already been incorporated into clinical practice. It also discusses key challenges, including ethical issues such as patient data privacy, algorithmic bias, and the transparency problem of AI models. The article emphasizes the necessity of integrating AI into medicine in a manner that ensures safety and trustworthiness, while underscoring the importance of educating healthcare professionals about AI. Artificial intelligence offers the potential to enhance diagnostic accuracy, the efficiency of care, and support for clinical decision-making, with optimal outcomes being achieved through collaboration between physicians and AI systems.

• MeSH
• Algorithms MeSH
• Medicine * MeSH
• Humans MeSH
• Nephrology MeSH
• Artificial Intelligence * ethics   MeSH
• Large Language Models MeSH
• Computer Security MeSH
• Check Tag
• Humans MeSH
• Publication type
• Review MeSH

The introduction of ChatGPT3 in 2023 disrupted the field of artificial intelligence (AI). ChatGPT uses large language models (LLMs) but has no access to copyrighted material including scientific articles and books. This review is limited by the lack of access to: (1) prior peer-reviewed articles and (2) proprietary information owned by the companies. Despite these limitations, the article reviews the use of LLMs in the publishing of scientific articles. The first use was plagiarism software. The second use by the American Psychological Association and Elsevier helped their journal editors to screen articles before their review. These two publishers have in common a large number of copyrighted journals and textbooks but, more importantly, a database of article abstracts. Elsevier is the largest of the five large publishing houses and the only one with a database of article abstracts developed to compete with the bibliometric experts of the Web of Science. The third use and most relevant, Scopus AI, was announced on 16 January 2024, by Elsevier; a version of ChatGPT-3.5 was trained using Elsevier copyrighted material written since 2013. Elsevier's description suggests to the authors that Scopus AI can write review articles or the introductions of original research articles with no human intervention. The editors of non-Elsevier journals not willing to approve the use of Scopus AI for writing scientific articles have a problem on their hands; they will need to trust that the authors who have submitted articles have not lied and have not used Scopus AI at all.

• MeSH
• Humans MeSH
• Periodicals as Topic MeSH
• Writing MeSH
• Publishing * standards   MeSH
• Artificial Intelligence * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Review MeSH

Úvod a cíl: Plně digitální workflow začíná ovládat naše ordinace. Přesnost a správnost některých intraorálních skenerů je nejenom dostatečná, ale výrazně překonává klasickou technologii otiskování (sádrový model) pro účely malých protetických rekonstrukcí. U velkých rekonstrukcí je ale situace zcela jiná. Cílem tohoto přehledu bylo shrnout současné poznatky o používaných technologiích intraorálních skenerů a měření jejich přesnosti. Dalším cílem bylo zhodnocení pomůcek/přípravků a postupů zpřesňujících intraorální skenování u velkých fixních protetických rekonstrukcí. Metodika: V databázích PubMed/Medline, Scopus a Embase bylo provedeno vyhledávání na základě klíčových slov: „Intraoral scanner“, „CAD/CAM“, „Trueness“, „Precision“, „Optical impression“, „Custom-made measuring device“, „Guided implant scanning“, „Continuous scan strategy“. Výsledky byly omezeny na články publikované v anglickém jazyce v letech 2010–2024. Výsledky: Kritéria pro zařazení do našeho článku splňovalo 37 publikací. Článků popisujících technologie, se kterými pracují dostupné intraorální skenery, bylo velmi málo. Publikací, které se zaměřovaly na zpřesnění intraorálního skenovaní pomocí nových postupů nebo přípravků, bylo 21. Zbylé zahrnuté články se zabývaly srovnáváním přesnosti intraorálních skenerů mezi různými výrobky nebo srovnáním s tradičními výrobními postupy. Většina studií porovnávajících přesnost intraorálních skenerů dříve využívala měření vzdálenosti a úhlové chyby. V novějších studiích převládá metoda překrývání povrchových dat získaných 3D skenery. Pouze jedna studie využívá pyramid replacement method s Prokrustovou analýzou. Závěr: Článků zabývajících se principem intraorálních skenerů je velmi málo a ve stomatologických časopisech jde o raritu. Z analýzy dostupné literatury vyplývá, že možností zpřesnění intraorálního skenu je více. Jedná se zejména o optimalizaci trasy skenování a zapojení jiných přístrojů bez skládací chyby do protetických postupů. Nadějně vypadají zejména extraorální skenery, a hlavně zapojení protetických laboratorních skenerů. Zmenšení deformace intraorálních skenů pomocí různých přípravků pravděpodobně nepřinese požadované zpřesnění.

Introduction and aim: A fully digital workflow is increasingly dominating our surgeries. For small prosthetic reconstructions on teeth or implants, the precision and trueness of certain intraoral scanners are not only sufficient, but significantly better than the conventional technology – dental impression/plaster model. A completely different situation arises with large reconstructions. The aim of this literature review was to summarize the current knowledge on intraoral scanner technologies and their accuracy measurements. Another aim was to evaluate devices and procedures for improving the accuracy of intraoral scans in large fixed prosthetic reconstructions. Methods: The PubMed/Medline, Scopus, and Embase databases were searched using the following keywords: “Intraoral scanner”, “CAD/CAM”, “Trueness”, “Precision”, “Optical impression”, “Custom-made measuring device”, “Guided implant scanning”, “Continuous scan strategy”. The results were limited to articles published in the English language between 2010 and 2024. Results: Thirty-seven publications met the inclusion criteria. There are very few articles describing the technology used by currently available intraoral scanners. Twenty-one publications focused on improving the accuracy of intraoral scanning using new procedures or devices. The remainder of the included articles compared the accuracy of intraoral scanners across different products or compared to traditional prosthetic procedures. Most of the older studies comparing the accuracy of intraoral scanners used distance measurements and angular errors. In more recent studies, the method of superimposing surface data obtained by 3D scanners was predominant. Only one study employed the pyramid replacement method with Procrustean analysis. Conclusion: Articles addressing the principles of intraoral scanners are scarce and rarely found in dental journals. An analysis of the available literature shows that there are multiple options to improve the accuracy of intraoral scanning. These strategies primarily involve optimizing the scanning path and incorporating additional devices to avoid merging errors in the prosthetic workflow. Extraoral scanners and the use of prosthetic lab scanners are especially promising. Reducing the merging error of intraoral scans using different devices probably does not have the potential to ensure the required accuracy.

• MeSH
• Technology, Dental methods   MeSH
• Computer-Aided Design instrumentation   MeSH
• Radiography, Dental, Digital * methods   MeSH
• Humans MeSH
• Systematic Reviews as Topic MeSH
• Imaging, Three-Dimensional methods   MeSH
• Dental Prosthesis Design MeSH
• Check Tag
• Humans MeSH

The Satisfaction With Life Scale (SWLS) is a widely used self-report measure of subjective well-being, but studies of its measurement invariance across a large number of nations remain limited. Here, we utilised the Body Image in Nature (BINS) dataset-with data collected between 2020 and 2022 -to assess measurement invariance of the SWLS across 65 nations, 40 languages, gender identities, and age groups (N = 56,968). All participants completed the SWLS under largely uniform conditions. Multi-group confirmatory factor analysis indicated that configural and metric invariance was upheld across all nations, languages, gender identities, and age groups, suggesting that the unidimensional SWLS model has universal applicability. Full scalar invariance was achieved across gender identities and age groups. Based on alignment optimisation methods, partial scalar invariance was achieved across all but three national groups and across all languages represented in the BINS. There were large differences in latent SWLS means across nations and languages, but negligible-to-small differences across gender identities and age groups. Across nations, greater life satisfaction was significantly associated with greater financial security and being in a committed relationship or married. The results of this study suggest that the SWLS largely assesses a common unidimensional construct of life satisfaction irrespective of respondent characteristics (i.e., national group, gender identities, and age group) or survey presentation (i.e., survey language). This has important implications for the assessment of life satisfaction across nations and provides information that will be useful for practitioners aiming to promote subjective well-being internationally.

• MeSH
• Adult MeSH
• Factor Analysis, Statistical MeSH
• Gender Identity * MeSH
• Language MeSH
• Quality of Life MeSH
• Middle Aged MeSH
• Humans MeSH
• Adolescent MeSH
• Young Adult MeSH
• Personal Satisfaction * MeSH
• Surveys and Questionnaires MeSH
• Psychometrics methods   MeSH
• Aged MeSH
• Age Factors MeSH
• Self Report MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Adolescent MeSH
• Young Adult MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

BACKGROUND AND PURPOSE: Pediatric radiotherapy patients and their parents are usually aware of their need for radiotherapy early on, but they meet with a radiation oncologist later in their treatment. Consequently, they search for information online, often encountering unreliable sources. Large language models (LLMs) have the potential to serve as an educational pretreatment tool, providing reliable answers to their questions. We aimed to evaluate the responses provided by generative pre-trained transformers (GPT), the most popular subgroup of LLMs, to questions about pediatric radiation oncology. MATERIALS AND METHODS: We collected pretreatment questions regarding radiotherapy from patients and parents. Responses were generated using GPT-3.5, GPT-4, and fine-tuned GPT-3.5, with fine-tuning based on pediatric radiotherapy guides from various institutions. Additionally, a radiation oncologist prepared answers to these questions. Finally, a multi-institutional group of nine pediatric radiotherapy experts conducted a blind review of responses, assessing reliability, concision, and comprehensibility. RESULTS: The radiation oncologist and GPT-4 provided the highest-quality responses, though GPT-4's answers were often excessively verbose. While fine-tuned GPT-3.5 generally outperformed basic GPT-3.5, it often provided overly simplistic answers. Inadequate responses were rare, occurring in 4% of GPT-generated responses across all models, primarily due to GPT-3.5 generating excessively long responses. CONCLUSIONS: LLMs can be valuable tools for educating patients and their families before treatment in pediatric radiation oncology. Among them, only GPT-4 provides information of a quality comparable to that of a radiation oncologist, although it still occasionally generates poor-quality responses. GPT-3.5 models should be used cautiously, as they are more likely to produce inadequate answers to patient questions.

• Publication type
• Journal Article MeSH

ChatGPT is a chatbot that is based on the generative pretrained transformer architecture as an artificial inteligence-based large language model. Its widespread use in healthcare practice, research, and education seems to be (increasingly) inevitable. Also considering the relevant limitations regarding privacy, ethics, bias, legal, and validity, in this article, its use as a supplement (for sure not as a substitute for physicians) is discussed in light of the recent literature. Particularly, the "opinion" of ChatGPT about how it can help/harm physiatry is exemplified.

• MeSH
• Language MeSH
• Humans MeSH
• Dietary Supplements MeSH
• Physical and Rehabilitation Medicine * MeSH
• Educational Status MeSH
• Artificial Intelligence * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

Artificial Intelligence (AI) has evolved significantly over the past decades, from its early concepts in the 1950s to the present era of deep learning and natural language processing. Advanced large language models (LLMs), such as Chatbot Generative Pre-Trained Transformer (ChatGPT) is trained to generate human-like text responses. This technology has the potential to revolutionize various aspects of gastroenterology, including diagnosis, treatment, education, and decision-making support. The benefits of using LLMs in gastroenterology could include accelerating diagnosis and treatment, providing personalized care, enhancing education and training, assisting in decision-making, and improving communication with patients. However, drawbacks and challenges such as limited AI capability, training on possibly biased data, data errors, security and privacy concerns, and implementation costs must be addressed to ensure the responsible and effective use of this technology. The future of LLMs in gastroenterology relies on the ability to process and analyse large amounts of data, identify patterns, and summarize information and thus assist physicians in creating personalized treatment plans. As AI advances, LLMs will become more accurate and efficient, allowing for faster diagnosis and treatment of gastroenterological conditions. Ensuring effective collaboration between AI developers, healthcare professionals, and regulatory bodies is essential for the responsible and effective use of this technology. By finding the right balance between AI and human expertise and addressing the limitations and risks associated with its use, LLMs can play an increasingly significant role in gastroenterology, contributing to better patient care and supporting doctors in their work.

• MeSH
• Deep Learning MeSH
• Gastroenterology * MeSH
• Humans MeSH
• Artificial Intelligence * MeSH
• Natural Language Processing * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Review MeSH

BACKGROUND: The topic of adolescent mental health is currently a subject of much debate due to the increasing prevalence of mental health problems among this age group. Therefore, it is crucial to have high-quality and validated mental well-being measurement tools. While such tools do exist, they are often not tailored specifically to adolescents and are not available in Czech language. The aim of this study is to validate and test the Czech version of the Short Warwick-Edinburgh Mental Well-Being Scale (SWEMWBS) on a large sample of Czech adolescents aged 15 to 18 years. METHODS: The analysis is based on data from the first wave of the Czech Education Panel Survey (CZEPS) and was mainly conducted using Item Response Theory (IRT), which is the most appropriate method for this type of analysis. Specifically, the Graded Response Model (GRM) was applied to the data. This comprehensive validation study also included reliability and three types of validity (construct, convergent and criterion) testing. RESULTS: The study found that the Czech version of the SWEMWBS for adolescents aged 15 to 18 years (N = 22,498) has good quality and psychometric properties. The data was analysed using the GRM model as it met the assumptions for the use of IRT. The estimated parameter values by GRM demonstrated good discriminant and informative power for all items, except for item 7, which showed poorer results compared to the others. However, excluding it from the scale would not enhance the overall quality of the scale. The five-category response scale functions effectively. Additionally, the results demonstrated high reliability, and all types of validity tested were also confirmed. CONCLUSIONS: The Czech version of the SWEMWBS for adolescents has been validated as a psychometrically sound, reliable and valid instrument for measuring mental well-being. It can therefore be used with confidence in future studies.

• MeSH
• Mental Health * MeSH
• Humans MeSH
• Adolescent MeSH
• Surveys and Questionnaires standards   MeSH
• Psychometrics * MeSH
• Reproducibility of Results MeSH
• Check Tag
• Humans MeSH
• Adolescent MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Validation Study MeSH
• Geographicals
• Czech Republic MeSH

In 2019, Gaming Disorder (GD) was acknowledged as an official diagnosis by the World Health Organization. The Gaming Disorder Test (GDT) is the most widely used tool to measure GD; however, due to its novelty, various measurement properties are still unexplored, and the number of validated language variants is still limited. The present study is the first to assess the psychometric properties of the Czech version of the GDT. Further, it focuses on its temporal prevalence and stability, gaming genre invariance, and criterion validity. A large-scale sample of adult Czech gamers collected at two points within nine months was analysed - T1 N = 5356; T2 N = 6077; longitudinal sample N = 1430. Confirmatory factor analysis (CFA), structural equation modelling (SEM), and multigroup CFA were employed to assess the measurement invariance. The study confirmed the one-factor structure of the GDT and showed that it is invariant across preferred gaming genres and the time of data collection. It showed a negative relationship with life satisfaction and a positive relationship with anxiety, even when controlling for their mutual relationships. The prevalence in the longitudinal sample was equal to or below 1.9% in each wave, but only 0.5% in the longitudinal sample (hence n = 7 participants fulfilled in both waves the criteria for GD). The study suggests that the Czech version of the GDT has good psychometric properties, including temporal stability and invariance across gaming genres, so it is suitable for the survey type and epidemiological investigation of the ICD-11's Gaming Disorder.

• MeSH
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Longitudinal Studies MeSH
• Adolescent MeSH
• Young Adult MeSH
• Internet Addiction Disorder diagnosis   epidemiology   MeSH
• Prevalence MeSH
• Psychiatric Status Rating Scales standards   MeSH
• Psychometrics * standards   instrumentation   MeSH
• Reproducibility of Results MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Adolescent MeSH
• Young Adult MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Geographicals
• Czech Republic MeSH