• MeSH
• generativní umělá inteligence * MeSH
• komunikace MeSH
• praktické lékařství MeSH
• teoretické modely MeSH
• využití lékařské informatiky MeSH
• zabezpečení počítačových systémů MeSH

BACKGROUND: Use of artificial intelligence (AI) in rare diseases has grown rapidly in recent years. In this review we have outlined the most common machine-learning and deep-learning methods currently being used to classify and analyse large amounts of data, such as standardized images or specific text in electronic health records. To illustrate how these methods have been adapted or developed for use with rare diseases, we have focused on Fabry disease, an X-linked genetic disorder caused by lysosomal α-galactosidase. A deficiency that can result in multiple organ damage. METHODS: We searched PubMed for articles focusing on AI, rare diseases, and Fabry disease published anytime up to 08 January 2025. Further searches, limited to articles published between 01 January 2021 and 31 December 2023, were also performed using double combinations of keywords related to AI and each organ affected in Fabry disease, and AI and rare diseases. RESULTS: In total, 20 articles on AI and Fabry disease were included. In the rare disease field, AI methods may be applied prospectively to large populations to identify specific patients, or retrospectively to large data sets to diagnose a previously overlooked rare disease. Different AI methods may facilitate Fabry disease diagnosis, help monitor progression in affected organs, and potentially contribute to personalized therapy development. The implementation of AI methods in general healthcare and medical imaging centres may help raise awareness of rare diseases and prompt general practitioners to consider these conditions earlier in the diagnostic pathway, while chatbots and telemedicine may accelerate patient referral to rare disease experts. The use of AI technologies in healthcare may generate specific ethical risks, prompting new AI regulatory frameworks aimed at addressing these issues to be established in Europe and the United States. CONCLUSION: AI-based methods will lead to substantial improvements in the diagnosis and management of rare diseases. The need for a human guarantee of AI is a key issue in pursuing innovation while ensuring that human involvement remains at the centre of patient care during this technological revolution.

• MeSH
• Fabryho nemoc * diagnóza   MeSH
• lidé MeSH
• umělá inteligence * MeSH
• vzácné nemoci * diagnóza   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

AIMS: The aim of this study was to compare the clinical decision-making for benzodiazepine deprescribing between a healthcare provider (HCP) and an artificial intelligence (AI) chatbot GPT4 (ChatGPT-4). METHODS: We analysed real-world data from a Croatian cohort of community-dwelling benzodiazepine patients (n = 154) within the EuroAgeism H2020 ESR 7 project. HCPs evaluated the data using pre-established deprescribing criteria to assess benzodiazepine discontinuation potential. The research team devised and tested AI prompts to ensure consistency with HCP judgements. An independent researcher employed ChatGPT-4 with predetermined prompts to simulate clinical decisions for each patient case. Data derived from human-HCP and ChatGPT-4 decisions were compared for agreement rates and Cohen's kappa. RESULTS: Both HPC and ChatGPT identified patients for benzodiazepine deprescribing (96.1% and 89.6%, respectively), showing an agreement rate of 95% (κ = .200, P = .012). Agreement on four deprescribing criteria ranged from 74.7% to 91.3% (lack of indication κ = .352, P < .001; prolonged use κ = .088, P = .280; safety concerns κ = .123, P = .006; incorrect dosage κ = .264, P = .001). Important limitations of GPT-4 responses were identified, including 22.1% ambiguous outputs, generic answers and inaccuracies, posing inappropriate decision-making risks. CONCLUSIONS: While AI-HCP agreement is substantial, sole AI reliance poses a risk for unsuitable clinical decision-making. This study's findings reveal both strengths and areas for enhancement of ChatGPT-4 in the deprescribing recommendations within a real-world sample. Our study underscores the need for additional research on chatbot functionality in patient therapy decision-making, further fostering the advancement of AI for optimal performance.

• MeSH
• benzodiazepiny škodlivé účinky   MeSH
• depreskripce * MeSH
• klinické rozhodování MeSH
• lidé MeSH
• umělá inteligence * MeSH
• zdravotnický personál MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem 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
• jazyk (prostředek komunikace) MeSH
• lidé MeSH
• potravní doplňky MeSH
• rehabilitační lékařství * MeSH
• stupeň vzdělání MeSH
• umělá inteligence * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Bez ohledu na to, zda jste čerstvý absolvent nebo zkušený profesionál, tato kniha vám pomůže zvýšit šance na získání vysněné práce. Pomůže vám zdokonalit své dovednosti při hledání zaměstnání v dnešní rychle se měnící pracovní sféře. Naučíte se jak vytvořit poutavý LinkedIn profil, efektivní životopis a jak využít sílu sociálních sítí.; Osvědčené rady a strategie, jak získat zaměstnání. Příručka

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
• gastroenterologie * MeSH
• lidé MeSH
• umělá inteligence * MeSH
• zpracování přirozeného jazyka * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

This mini-review aims to identify major research trends, models, and theories and provide specific pedagogical implications for teaching when using chatbots in EFL classes. This study follows the guidelines of the PRISMA methodology and searches for open-access empirical studies in two reputable databases, Web of Science and Scopus. The results of this mini-review confirm the findings of other research studies, which show that the present research on the use of chatbots in university EFL settings focuses on their effectiveness, motivation, satisfaction, exposure, and assessment. The key contribution of this study lies in its evaluation of the chatbot's potential in applying and integrating the existing theories and concepts used in EFL teaching and learning, such as CEFR, mind mapping, or self-regulatory learning theory. This will address the gap in the literature because no previous review study has conducted such an analysis. Overall, the findings of this mini-review contribute with their specific pedagogical implications and methods to the effective use of chatbots in the EFL environment, be it formal or informal.

• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

Chatbots without artificial intelligence can play the role of practical and easy-to-implement learning objects in e-learning environments, allowing a reduction in social or psychological isolation. This research, with a sample of 79 students, explores the principles that need to be followed in designing this kind of chatbot in education in order to ensure an acceptable outcome for students. Research has shown that students interacting with a chatbot without artificial intelligence expect similar psychological and communicative responses to those of a live human, project the characteristics of the chatbot from the dialogue, and are taken aback when the chatbot does not understand or cannot help them sufficiently. The study is based on a design through research approach, in which students in information studies and library science interacted with a specific chatbot focused on information retrieval, and recorded their experiences and feelings in an online questionnaire. The study intends to find principles for the design of chatbots without artificial intelligence so that students feel comfortable interacting with them.

• Publikační typ
• časopisecké články MeSH

Artificial intelligence-driven voice technology deployed on mobile phones and smart speakers has the potential to improve patient management and organizational workflow. Voice chatbots have been already implemented in health care-leveraging innovative telehealth solutions during the COVID-19 pandemic. They allow for automatic acute care triaging and chronic disease management, including remote monitoring, preventive care, patient intake, and referral assistance. This paper focuses on the current clinical needs and applications of artificial intelligence-driven voice chatbots to drive operational effectiveness and improve patient experience and outcomes.

• MeSH
• chronická nemoc terapie   MeSH
• COVID-19 * MeSH
• hlas * MeSH
• komunikace * MeSH
• konziliární vyšetření a konzultace MeSH
• lidé MeSH
• mobilní telefon MeSH
• pandemie MeSH
• péče o pacienty v kritickém stavu metody   MeSH
• poskytování zdravotní péče metody   MeSH
• software pro rozpoznávání řeči * MeSH
• telemedicína metody   MeSH
• třídění pacientů MeSH
• umělá inteligence * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

• MeSH
• COVID-19 MeSH
• Publikační typ
• novinové články MeSH
• zprávy MeSH