knowledge representation and management
Dotaz
Zobrazit nápovědu
BACKGROUND: As the healthcare sector evolves, Artificial Intelligence's (AI's) potential to enhance laboratory medicine is increasingly recognized. However, the adoption rates and attitudes towards AI across European laboratories have not been comprehensively analyzed. This study aims to fill this gap by surveying European laboratory professionals to assess their current use of AI, the digital infrastructure available, and their attitudes towards future implementations. METHODS: We conducted a methodical survey during October 2023, distributed via EFLM mailing lists. The survey explored six key areas: general characteristics, digital equipment, access to health data, data management, AI advancements, and personal perspectives. We analyzed responses to quantify AI integration and identify barriers to its adoption. RESULTS: From 426 initial responses, 195 were considered after excluding incomplete and non-European entries. The findings revealed limited AI engagement, with significant gaps in necessary digital infrastructure and training. Only 25.6 % of laboratories reported ongoing AI projects. Major barriers included inadequate digital tools, restricted access to comprehensive data, and a lack of AI-related skills among personnel. Notably, a substantial interest in AI training was expressed, indicating a demand for educational initiatives. CONCLUSIONS: Despite the recognized potential of AI to revolutionize laboratory medicine by enhancing diagnostic accuracy and efficiency, European laboratories face substantial challenges. This survey highlights a critical need for strategic investments in educational programs and infrastructure improvements to support AI integration in laboratory medicine across Europe. Future efforts should focus on enhancing data accessibility, upgrading technological tools, and expanding AI training and literacy among professionals. In response, our working group plans to develop and make available online training materials to meet this growing educational demand.
- MeSH
- klinické laboratoře MeSH
- lidé MeSH
- průzkumy a dotazníky MeSH
- umělá inteligence * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Evropa MeSH
Závěrečná práce NCO NZO
1 svazek : tabulky, grafy ; 30 cm
- Klíčová slova
- naivní model, transfuzní přípravek,
- MeSH
- dárci krve MeSH
- krevní bankovnictví organizace a řízení MeSH
- strojové učení zásobování a distribuce MeSH
- techniky plánování MeSH
- umělá inteligence zásobování a distribuce MeSH
- vybavení a zásoby nemocnice MeSH
- Konspekt
- Patologie. Klinická medicína
- NLK Publikační typ
- závěrečné práce
PURPOSE: Fuchs endothelial corneal dystrophy (FECD) is a common, age-related cause of visual impairment. This systematic review synthesizes evidence from the literature on artificial intelligence (AI) models developed for the diagnosis and management of FECD. METHODS: We conducted a systematic literature search in MEDLINE, PubMed, Web of Science, and Scopus from January 1, 2000, to June 31, 2024. Full-text studies utilizing AI for various clinical contexts of FECD management were included. Data extraction covered model development, predicted outcomes, validation, and model performance metrics. We graded the included studies using the Quality Assessment of Diagnostic Accuracies Studies 2 tool. This review adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations. RESULTS: Nineteen studies were analyzed. Primary AI algorithms applied in FECD diagnosis and management included neural network architectures specialized for computer vision, utilized on confocal or specular microscopy images, or anterior segment optical coherence tomography images. AI was employed in diverse clinical contexts, such as assessing corneal endothelium and edema and predicting post-corneal transplantation graft detachment and survival. Despite many studies reporting promising model performance, a notable limitation was that only three studies performed external validation. Bias introduced by patient selection processes and experimental designs was evident in the included studies. CONCLUSIONS: Despite the potential of AI algorithms to enhance FECD diagnosis and prognostication, further work is required to evaluate their real-world applicability and clinical utility. TRANSLATIONAL RELEVANCE: This review offers critical insights for researchers, clinicians, and policymakers, aiding their understanding of existing AI research in FECD management and guiding future health service strategies.
- MeSH
- Fuchsova endoteliální dystrofie * diagnóza terapie MeSH
- lidé MeSH
- optická koherentní tomografie metody MeSH
- umělá inteligence * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- systematický přehled 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
Background/Objectives: Health and social care systems around the globe are currently undergoing a transformation towards personalized, preventive, predictive, participative precision medicine (5PM), considering the individual health status, conditions, genetic and genomic dispositions, etc., in personal, social, occupational, environmental, and behavioral contexts. This transformation is strongly supported by technologies such as micro- and nanotechnologies, advanced computing, artificial intelligence, edge computing, etc. Methods: To enable communication and cooperation between actors from different domains using different methodologies, languages, and ontologies based on different education, experiences, etc., we have to understand the transformed health ecosystem and all its components in terms of structure, function and relationships in the necessary detail, ranging from elementary particles up to the universe. In this way, we advance design and management of the complex and highly dynamic ecosystem from data to knowledge level. The challenge is the consistent, correct, and formalized representation of the transformed health ecosystem from the perspectives of all domains involved, representing and managing them based on related ontologies. The resulting business viewpoint of the real-world ecosystem must be interrelated using the ISO/IEC 21838 Top Level Ontologies standard. Thereafter, the outcome can be transformed into implementable solutions using the ISO/IEC 10746 Open Distributed Processing Reference Model. Results: The model and framework for this system-oriented, architecture-centric, ontology-based, policy-driven approach have been developed by the first author and meanwhile standardized as ISO 23903 Interoperability and Integration Reference Architecture. The formal representation of any ecosystem and its development process including examples of practical deployment of the approach, are presented in detail. This includes correct systems and standards integration and interoperability solutions. A special issue newly addressed in the paper is the correct and consistent formal representation Conclusions: of all components in the development process, enabling interoperability between and integration of any existing representational artifacts such as models, work products, as well as used terminologies and ontologies. The provided solution is meanwhile mandatory at ISOTC215, CEN/TC251 and many other standards developing organization in health informatics for all projects covering more than just one domain.
- Publikační typ
- časopisecké články MeSH
- MeSH
- časná detekce nádoru MeSH
- dávkové mechanismy MeSH
- dostupnost zdravotnických služeb organizace a řízení MeSH
- ekonomika a organizace zdravotní péče MeSH
- léčivé přípravky ekonomika zásobování a distribuce MeSH
- lékařská informatika MeSH
- lékové předpisy ekonomika MeSH
- lidé MeSH
- nádory prostaty diagnóza MeSH
- nemocnice všeobecné organizace a řízení MeSH
- osteoporóza diagnóza MeSH
- řízení veřejného zdraví * MeSH
- řízení zdravotnických informací MeSH
- služby péče o duševní zdraví organizace a řízení MeSH
- týmová péče o pacienty MeSH
- umělá inteligence MeSH
- zdravotnické informační systémy MeSH
- zdravotničtí záchranáři MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- novinové články MeSH
The traditional healthcare model is focused on diseases (medicine and natural science) and does not acknowledge patients' resources and abilities to be experts in their own lives based on their lived experiences. Improving healthcare safety, quality, and coordination, as well as quality of life, is an important aim in the care of patients with chronic conditions. Person-centered care needs to ensure that people's values and preferences guide clinical decisions. This paper reviews current knowledge to develop (1) digital care pathways for rhinitis and asthma multimorbidity and (2) digitally enabled, person-centered care.1 It combines all relevant research evidence, including the so-called real-world evidence, with the ultimate goal to develop digitally enabled, patient-centered care. The paper includes (1) Allergic Rhinitis and its Impact on Asthma (ARIA), a 2-decade journey, (2) Grading of Recommendations, Assessment, Development and Evaluation (GRADE), the evidence-based model of guidelines in airway diseases, (3) mHealth impact on airway diseases, (4) From guidelines to digital care pathways, (5) Embedding Planetary Health, (6) Novel classification of rhinitis and asthma, (7) Embedding real-life data with population-based studies, (8) The ARIA-EAACI (European Academy of Allergy and Clinical Immunology) strategy for the management of airway diseases using digital biomarkers, (9) Artificial intelligence, (10) The development of digitally enabled, ARIA person-centered care, and (11) The political agenda. The ultimate goal is to propose ARIA 2024 guidelines centered around the patient to make them more applicable and sustainable.
Understanding the communication dynamics between vaccine-hesitant parents and healthcare professionals (HCPs) is vital for addressing parent concerns and promoting informed decision-making. This paper focuses on strategies used by HCPs to communicate with vaccine-hesitant parents. It draws on empirical evidence generated as part of the international project VAX-TRUST. More specifically, 60 hours of observations were carried out in three different pediatric practices during vaccination-related visits, and 19 physicians and nurses were interviewed. We focused on the specific context of the Czech Republic, which represents a country with a mandatory vaccination system and in which children's immunization is the responsibility of pediatric general practitioners. We demonstrate that the dynamics between parents and HCPs and their willingness to invest time in the vaccination discussion are influenced by how HCPs categorize and label parents. Furthermore, we outline some of the different strategies HCPs employ while addressing concerns regarding vaccination. We identified two different strategies HCPs use to manage the fears of vaccine-hesitant parents. The first strategy focused on the communication of risks associated with vaccination (and lack thereof). HCPs used a variety of discursive practices to familiarize the unfamiliar risks of vaccine-preventable diseases (by mobilizing representations that are part of collective memory, incorporating personal experiences to materialize the presence of risk and the confidence in the safety of vaccines and by situating risk as embedded in everyday processes and integral to the uncertainty of the global world). The second strategy involved the conscious employment of medical procedures that may contribute to reducing vaccination fears.
- MeSH
- dítě MeSH
- dospělí MeSH
- komunikace * MeSH
- lidé MeSH
- odkládání očkování * psychologie statistika a číselné údaje MeSH
- pacientův souhlas se zdravotní péčí psychologie MeSH
- rodiče * psychologie MeSH
- rozhodování MeSH
- vakcinace * psychologie MeSH
- vakcíny aplikace a dávkování MeSH
- vztahy mezi odborníkem a rodinou MeSH
- zdraví - znalosti, postoje, praxe MeSH
- zdravotnický personál * psychologie MeSH
- Check Tag
- dítě MeSH
- dospělí MeSH
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Česká republika MeSH
Health and social care systems around the globe currently undergo a transformation towards personalized, preventive, predictive, participative precision medicine (5PM), considering the individual health status, conditions, genetic and genomic dispositions, etc., in personal, social, occupational, environmental and behavioral context. This transformation is strongly supported by technologies such as micro- and nanotechnologies, advanced computing, artificial intelligence, edge computing, etc. For enabling communication and cooperation between actors from different domains using different methodologies, languages and ontologies based on different education, experiences, etc., we have to understand the transformed health ecosystems and all its components in structure, function and relationships in the necessary detail ranging from elementary particles up to the universe. That way, we advance design and management of the complex and highly dynamic ecosystem from data to knowledge level. The challenge is the consistent, correct and formalized representation of the transformed health ecosystem from the perspectives of all domains involved, representing and managing them based on related ontologies. The resulting business view of the real-world ecosystem must be interrelated using the ISO/IEC 21838 Top Level Ontologies standard. Thereafter, the outcome can be transformed into implementable solutions using the ISO/IEC 10746 Open Distributed Processing Reference Model. Model and framework for this system-oriented, architecture-centric, ontology-based, policy-driven approach have been developed by the first author and meanwhile standardized as ISO 23903 Interoperability and Integration Reference Architecture.
- MeSH
- individualizovaná medicína * MeSH
- lidé MeSH
- umělá inteligence MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Technické riešenie realizácie procesov zameraných na preverovanie zdravotného stavu pri bežnej hraničnej kontrole pred vstupom do EÚ vykazuje značne kom- plexný charakter. Je nutné spoľahlivo a efektívne in- terpretovať odozvy na špecifické podnety v rámci rozličných podmienok prostredia a prípadného výsky- tu bezpečnostného rizika, zahŕňajúc špecifické náro- ky na vybavenie a personálne osadenie v rámci hra- ničného režimu. Komplexný charakter riadenia hraníc doplnený o problematiku zdravotných kontrol viedol k návrhu Mobilného, dátového, odberového a analy- tického centra (MDOAC). V rámci riešenia MDOAC je navrhnuté aktívne využívanie moderných rádiologických metód a techník podporených rýchlym spracovaním a vyhodnotením za pomoci systémov umelej inteligencie. Výhody, ktoré poskytuje synergia medzi oblasťou rádiológie a modernými technológia- mi zbierania a vyhodnocovania dát v reálnom čase, spolu s potrebami minimalizácie fyzického kontaktu medzi potenciálne rizikovým subjektom vyšetrenia a poverenými príslušníkmi Policajného zboru, resp. vyšetrujúcim personálom z radov rádiológov a rádiologických technikov, vykazujú optimálny pod- klad pre naplnenie podmienok a úloh zadefinova- ných v Európskej legislatíve a to predovšetkým v Novom pakte o migrácii a azyle. Realizovaná ana- lýza potrieb mobilného prevedenia navrhovaného centra a zároveň analýza jeho trvalého umiestnenia v stávajúcich priestoroch a areáloch hraničných prie- chodov poukazuje na možnosti zamerania výskumu aj smerom na stacionárne riešenie s orientáciou na využitie všetkých potenciálnych výhod
The technical solution for the implementation of pro- cesses aimed at checking the state of health during normal border control before entering the EU shows a considerably complex nature. It is necessary to in- terpret responses reliably and efficiently to specific stimuli within various environmental conditions and the eventual occurrence of a security risk, summari- zing the specific requirements for equipment and staffing within the border regime. The complex na- ture of border management, supplemented by the issue of health checks, led to proposal of the Mobile, Data, Collection and Analysis Center (MDOAC). As part of the MDOAC solution, the active use of mo- dern radiological methods and techniques supported by rapid processing and evaluation with the help of artificial intelligence systems is proposed. The advan- tages provided by the synergy between the field of radiology and modern technologies of data collec- tion and evaluation in real time, together with the need to minimize physical contact between the po- tentially risky subject of the examination and autho- rized members of the Police Force, resp. examining personnel from the ranks of radiologists and radio- graphers, they show the optimal basis for fulfilling the conditions and tasks defined in European legis- lation, especially in the New Pact on Migration and Asylum. The realized analysis of the needs of the mobile version of the proposed center and at the same time the analysis of its permanent location in the existing premises and areas of the border cros- sings point to the possibilities of focusing the rese- arch also in the direction of a stationary solution with an orientation the use of all potential advanta- ges.
