• MeSH
• Organization and Administration standards   MeSH
• Fraud prevention & control   MeSH
• Social Control Policies standards   MeSH
• Publication type
• Guideline MeSH

• Conspectus
• Metrologie. Standardizace
• NML Fields
• sociologie

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.

BACKGROUND: Advancements in artificial intelligence (AI) and machine learning (ML) have revolutionized the medical field and transformed translational medicine. These technologies enable more accurate disease trajectory models while enhancing patient-centered care. However, challenges such as heterogeneous datasets, class imbalance, and scalability remain barriers to achieving optimal predictive performance. METHODS: This study proposes a novel AI-based framework that integrates Gradient Boosting Machines (GBM) and Deep Neural Networks (DNN) to address these challenges. The framework was evaluated using two distinct datasets: MIMIC-IV, a critical care database containing clinical data of critically ill patients, and the UK Biobank, which comprises genetic, clinical, and lifestyle data from 500,000 participants. Key performance metrics, including Accuracy, Precision, Recall, F1-Score, and AUROC, were used to assess the framework against traditional and advanced ML models. RESULTS: The proposed framework demonstrated superior performance compared to classical models such as Logistic Regression, Random Forest, Support Vector Machines (SVM), and Neural Networks. For example, on the UK Biobank dataset, the model achieved an AUROC of 0.96, significantly outperforming Neural Networks (0.92). The framework was also efficient, requiring only 32.4 s for training on MIMIC-IV, with low prediction latency, making it suitable for real-time applications. CONCLUSIONS: The proposed AI-based framework effectively addresses critical challenges in translational medicine, offering superior predictive accuracy and efficiency. Its robust performance across diverse datasets highlights its potential for integration into real-time clinical decision support systems, facilitating personalized medicine and improving patient outcomes. Future research will focus on enhancing scalability and interpretability for broader clinical applications.

• MeSH
• Databases, Factual MeSH
• Humans MeSH
• Neural Networks, Computer MeSH
• Patient-Centered Care * MeSH
• Machine Learning * MeSH
• Translational Science, Biomedical MeSH
• Translational Research, Biomedical MeSH
• Artificial Intelligence * MeSH
• Treatment Outcome MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

To evaluate the clinical performance and safety of the ONIRY system for obstetric anal sphincter injuries (OASI) detection versus three-dimensional endoanal ultrasound (EAUS). A prospective, comparative, multicentre, international study. Poland, Czechia, Slovakia, and Spain. 152 women between the first moments up to 8 weeks after vaginal delivery. Participants underwent EAUS and were allocated to groups based on OASIS classification: A (no perineal tear), B (1st or 2nd degree tear), or C (3rd or 4th degree, anal sphincters affected). Electric impedance was measured in the anal canal using the ONIRY system. The primary endpoint was the diagnostic outcome of impedance spectroscopy versus EAUS. Adverse events were collected. Part II involved in silico modelling and 10-time 10-fold cross-validation for automated analysis. Accuracy, sensitivity, and specificity. 30 women were allocated to group A, 61 to group B, and 61 to group C. The diagnostic outcome was determined for 147 participants. The accuracy, sensitivity, and specificity of the ML-assisted impedance spectroscopy were 87.0 ± 0.5%, 90.6 ± 2.0%, and 84.6 ± 1.9%, respectively, compared with EAUS. After data cleaning, the performance metrics of the proposed final ML model for ONIRY were: 90.0 ± 0.4%, 90.0 ± 1.2%, and 90.0 ± 0.7%, respectively. No adverse device effects or deficiencies were observed. By enabling early identification of sphincter injuries, ML-assisted impedance spectroscopy facilitates timely diagnosis and intervention, potentially reducing long-term complications such as faecal incontinence. Its rapid, bedside application in obstetric settings supports immediate postpartum care, complementing digital rectal examination and optimizing clinical decision-making.

• MeSH
• Anal Canal * injuries   diagnostic imaging   MeSH
• Adult MeSH
• Dielectric Spectroscopy * methods   MeSH
• Obstetric Labor Complications diagnosis   diagnostic imaging   MeSH
• Humans MeSH
• Prospective Studies MeSH
• Machine Learning * MeSH
• Pregnancy MeSH
• Delivery, Obstetric * adverse effects   MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Pregnancy MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Multicenter Study MeSH
• Comparative Study MeSH

Space and time are fundamental attributes of the external world. Deciphering the brain mechanisms involved in processing the surrounding environment is one of the main challenges in neuroscience. This is particularly defiant when situations change rapidly over time because of the intertwining of spatial and temporal information. However, understanding the cognitive processes that allow coping with dynamic environments is critical, as the nervous system evolved in them due to the pressure for survival. Recent experiments have revealed a new cognitive mechanism called time compaction. According to it, a dynamic situation is represented internally by a static map of the future interactions between the perceived elements (including the subject itself). The salience of predicted interactions (e.g. collisions) over other spatiotemporal and dynamic attributes during the processing of time-changing situations has been shown in humans, rats, and bats. Motivated by this ubiquity, we study an artificial neural network to explore its minimal conditions necessary to represent a dynamic stimulus through the future interactions present in it. We show that, under general and simple conditions, the neural activity linked to the predicted interactions emerges to encode the perceived dynamic stimulus. Our results show that this encoding improves learning, memorization and decision making when dealing with stimuli with impending interactions compared to no-interaction stimuli. These findings are in agreement with theoretical and experimental results that have supported time compaction as a novel and ubiquitous cognitive process.

• MeSH
• Humans MeSH
• Brain physiology   MeSH
• Neural Networks, Computer * MeSH
• Memory physiology   MeSH
• Decision Making physiology   MeSH
• Learning physiology   MeSH
• Time Perception physiology   MeSH
• Space Perception physiology   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

BACKGROUND: Identification of real-time adverse drug reactions [ADRs] (as opposed to the risk of ADRs) in older poly-medicated people in primary care is a challenging task, often undertaken without an explicit strategy. This systematic review aims to evaluate replicable instruments and methods for identifying and addressing ADRs. METHODS: A systematic search was conducted in Medline, CINAHL, Scopus, Web of Science and Cochrane library, using controlled vocabulary (MeSH) and free-text terms. Randomised controlled trials (RCTs) implementing strategies to identify or resolve ADRs experienced by patients in primary care were included. Two reviewers independently screened studies, extracted data, and assessed the risk of bias using the Cochrane Risk of Bias tool. Discrepancies were resolved by discussion. RESULTS: From 2,182 unique records, 49 studies were identified for full review. Eight papers reporting results from 6 RCTs were included. All six trials utilised a list of medicine-related unwanted symptoms to identify ADRs. Two of three studies using adverse drug reaction questionnaires reported statistically significant increased rates of ADR reporting. Two of three studies that combined symptom questionnaires with prescriber consultations reported reductions in the number of health problems. Overall, results suggest that the three studies that described multidisciplinary collaborations using lists of ADRs plus prescriber reviews enhanced patient safety. However, the RCTs were unblinded and reported suboptimal retention. When considered as a whole, findings are equivocal and the data are too heterogenous to warrant any firm conclusions, beyond the need for more research to optimise strategies to safeguard patient wellbeing. IMPLICATIONS: Adaptable and scalable instruments with decision support are needed in primary care to identify and mitigate medicine-related harm in older poly-medicated people. The effectiveness of adverse drug reaction identification instruments, the value of comprehensive instruments, and the optimum method of delivery should be explored in multicentre trials.

• MeSH
• Humans MeSH
• Drug-Related Side Effects and Adverse Reactions * MeSH
• Primary Health Care * MeSH
• Randomized Controlled Trials as Topic MeSH
• Adverse Drug Reaction Reporting Systems MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Systematic Review MeSH

Navzdory existenci mezinárodních smluv a strategických dokumentů zavazujících vzdělávací systém v ČR k umožnění rovného přístupu ke vzdělávání pro každé dítě, je bezdůvodné uvolnění ze školní tělesné výchovy u nás často využívanou realitou. Tato legislativou vytvořená možnost má zásadní negativní konsekvence. Nadužívání uvolňování vede k vytváření bariér v přístupu značné skupiny žáků k pohybovým aktivitám a jejich vyloučení z části vzdělávání zaměřené na pohybový rozvoj v průběhu celého dalšího vzdělávání (tělesná výchova, plavecké a lyžařské kurzy, pohybové soutěže a akce). Žáci, u nichž z důvodů zdravotních limitů je potřeba věnovat somatickému rozvoji zvýšenou pozornost, s inaktivitou pracovat a hledat cesty k celoživotní motivaci, jsou tak vyloučeni nejen z tělesné výchovy, ale často i z dalších akcí školy. Zodpovědnost za rozhodnutí o vzdělávání žáků v tělesné výchově je kladena na lékaře, aniž by tito měli potřebné znalosti současných vzdělávacích norem a standardů a kapacitu pro jednotlivé děti vhodná vzdělávací doporučení vytvářet. I přes dostupnost podpory žáků se speciálními vzdělávacími potřebami, jsou stále více uvolňování z tělesné výchovy žáci s minimálními zdravotními limity. Cílem příspěvku je upozornit na současné uplatňování systému uvolňování z tělesné výchovy ve vztahu k právním aspektům a seznámit odbornou lékařskou veřejnost s možnostmi úprav a postupů v současném systému vzdělávání žáků se ztíženým přístupem k pohybovým aktivitám. Předkládaný příspěvek si dovoluje diskutovat nad některými pasážemi zákona č. 561/2004 Sb., o předškolním, základním, středním, vyšším odborném a jiném vzdělávání, ve znění pozdějších předpisů a s nimi související vyhláškou Ministerstva zdravotnictví České republiky č. 391/2013 Sb., o zdravotní způsobilosti k tělesné výchově a sportu, která si klade za cíl působit preventivně na ochranu zdraví nejen dětí, ale i dospělých v kontextu výkonnostního sportu, ale i volnočasových aktivit a školní tělesné výchovy. Negativně hodnotíme zejména skutečnost, že platné právní normy upravují možnost uvolnění ze školní tělesné výchovy bez nabídky alternativy s cílem podpory žáků při naplnění jejich vzdělávacích povinností.

Despite the existence of international treaties and strategic documents that oblige the education system in the Czech Republic to ensure equal access to education for every child, unjustified exemption from school physical education is a frequently used reality in our country. This possibility created by legislation has serious negative consequences. Excessive use of exemptions leads to the creation of barriers to access to physical activities for a significant group of pupils and to their exclusion from the physical development part of education throughout further education (physical education, swimming and skiing courses, physical competitions and events). Pupils whose health limitations require them to pay increased attention to somatic development, to work with inactivity and to seek ways to achieve lifelong motivation are thus excluded not only from physical education but often also from other school events. Responsibility for making decisions about pupils' physical education is delegated to doctors without the necessary knowledge of current educational norms and standards to be able to make appropriate educational recommendations for individual children. Despite the availability of support for pupils with special educational needs, pupils with minimal medical needs are increasingly being released from physical education. It is this inconsistency in the application of standards in relation to exemption from physical education that is the subject of our criticism. The aim of this paper is to highlight the current application of the system of release from physical education in relation to the legal aspects and to inform the medical professional community about the possibilities of adjustments and procedures in the current system of education of pupils with physical disabilities. The present paper takes the liberty to analyse some passages of Act No. 561/2004 Coll. on pre-school, primary, secondary, higher vocational and other education, as amended, and the related Decree of the Ministry of Health No. 391/2013 Coll. on medical fitness for physical education and sport, the aim of which is to have a preventive effect on the protection of the health of not only children, but also adults in connection with performance sports, as well as leisure activities and school physical education. In particular, we negatively assess the fact that the current legal norms regulate the possibility of release from school physical education without offering an alternative in order to support pupils in fulfilling their educational obligation.

• Keywords
• speciální vzdělávací potřeby,
• MeSH
• Child MeSH
• Humans MeSH
• Motor Activity MeSH
• Sedentary Behavior MeSH
• Physical Education and Training * legislation & jurisprudence   MeSH
• Check Tag
• Child MeSH
• Humans MeSH
• Publication type
• Review MeSH

PURPOSE OF REVIEW: The aim of the systematic review is to assess AI's capabilities in the genetics of prostate cancer (PCa) and bladder cancer (BCa) to evaluate target groups for such analysis as well as to assess its prospects in daily practice. RECENT FINDINGS: In total, our analysis included 27 articles: 10 articles have reported on PCa and 17 on BCa, respectively. The AI algorithms added clinical value and demonstrated promising results in several fields, including cancer detection, assessment of cancer development risk, risk stratification in terms of survival and relapse, and prediction of response to a specific therapy. Besides clinical applications, genetic analysis aided by the AI shed light on the basic urologic cancer biology. We believe, our results of the AI application to the analysis of PCa, BCa data sets will help to identify new targets for urological cancer therapy. The integration of AI in genomic research for screening and clinical applications will evolve with time to help personalizing chemotherapy, prediction of survival and relapse, aid treatment strategies such as reducing frequency of diagnostic cystoscopies, and clinical decision support, e.g., by predicting immunotherapy response. These factors will ultimately lead to personalized and precision medicine thereby improving patient outcomes.

• MeSH
• Biomarkers MeSH
• Humans MeSH
• Neoplasm Recurrence, Local genetics   MeSH
• Urinary Bladder Neoplasms * diagnosis   genetics   therapy   MeSH
• Prostate * MeSH
• Recurrence MeSH
• Artificial Intelligence MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Publication type
• Journal Article MeSH
• Review MeSH
• Systematic Review MeSH

Tento článek se zaměřuje na aktuální a komplexní přehled o stroke mimics (SM), jež představují výzvu pro diferenciální diagnostiku vzhledem k širokému spektru jejich příznaků podobných CMP. Uvádíme stručná epidemiologická data, klinický obraz a čtyři prediktivní škály vyvinuté pro diagnostiku SM, které byly identifikovány na základě literární rešerše: TeleStroke Mimic Score (TSM), FABS, simplified FABS (sFABS) a Khan score. Tyto validované nástroje mohou podpořit rychlé a efektivní rozhodování o léčbě v prostředí urgentního příjmu s cílem minimalizovat zpoždění v poskytování adekvátní péče pacientům s CMP. Rádi bychom upozornili na význam správného rozpoznání SM s ohledem na časovou citlivost rekanalizační léčby, důrazem na optimalizaci léčby a management pacientů s akutně vzniklými neurologickými příznaky.

The article presents a current and comprehensive review of stroke mimics (SM), which represent a challenge for differential diagnosis due to their wide range of similar symptoms with strokes. It delves into the brief epidemiology, clinical features, and four predictive scales for SM diagnosis, which were identified on the basis of a literature search: TeleStroke Mimic Score (TSM), FABS, simplified FABS (sFABS), and Khan score. These validated tools might support rapid and efficient decision-making regarding treatment in an emergency department setting with the goal of minimizing delays in providing adequate care to patients with stroke. We would like to highlight the importance of correct identification of SM given the time sensitivity of revascularization treatment, with a focus on optimizing treatment and management of patients with acute onset of neurological symptoms.

• Keywords
• stroke mimics,
• MeSH
• Stroke diagnosis   pathology   MeSH
• Diagnosis, Differential MeSH
• Humans MeSH
• Nervous System Diseases * diagnosis   epidemiology   classification   pathology   MeSH
• Neurologic Manifestations MeSH
• Neurologic Examination classification   MeSH
• Predictive Value of Tests * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Research Support, Non-U.S. Gov't MeSH
• Review MeSH

INTRODUCTION: The rapid advancement of artificial intelligence and big data analytics, including descriptive, diagnostic, predictive, and prescriptive analytics, has the potential to revolutionize many areas of medicine, including nephrology and dialysis. Artificial intelligence and big data analytics can be used to analyze large amounts of patient medical records, including laboratory results and imaging studies, to improve the accuracy of diagnosis, enhance early detection, identify patterns and trends, and personalize treatment plans for patients with kidney disease. Additionally, artificial intelligence and big data analytics can be used to identify patients' treatment who are not receiving adequate care, highlighting care inefficiencies in the dialysis provider, optimizing patient outcomes, reducing healthcare costs, and consequently creating values for all the involved stakeholders. OBJECTIVES: We present the results of a comprehensive survey aimed at exploring the attitudes of European physicians from eight countries working within a major hemodialysis network (Fresenius Medical Care NephroCare) toward the application of artificial intelligence in clinical practice. METHODS: An electronic survey on the implementation of artificial intelligence in hemodialysis clinics was distributed to 1,067 physicians. Of the 1,067 individuals invited to participate in the study, 404 (37.9%) professionals agreed to participate in the survey. RESULTS: The survey showed that a substantial proportion of respondents believe that artificial intelligence has the potential to support physicians in reducing medical malpractice or mistakes. CONCLUSION: While artificial intelligence's potential benefits are recognized in reducing medical errors and improving decision-making, concerns about treatment plan consistency, personalization, privacy, and the human aspects of patient care persist. Addressing these concerns will be crucial for successfully integrating artificial intelligence solutions in nephrology practice.

• MeSH
• Renal Dialysis MeSH
• Humans MeSH
• Nephrology * MeSH
• Nephrologists MeSH
• Surveys and Questionnaires MeSH
• Artificial Intelligence * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH