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
• Laboratories, Clinical MeSH
• Humans MeSH
• Surveys and Questionnaires MeSH
• Artificial Intelligence * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Geographicals
• Europe MeSH

The topic of the diagnosis of phaeochromocytomas remains highly relevant because of advances in laboratory diagnostics, genetics, and therapeutic options and also the development of imaging methods. Computed tomography still represents an essential tool in clinical practice, especially in incidentally discovered adrenal masses; it allows morphological evaluation, including size, shape, necrosis, and unenhanced attenuation. More advanced post-processing tools to analyse digital images, such as texture analysis and radiomics, are currently being studied. Radiomic features utilise digital image pixels to calculate parameters and relations undetectable by the human eye. On the other hand, the amount of radiomic data requires massive computer capacity. Radiomics, together with machine learning and artificial intelligence in general, has the potential to improve not only the differential diagnosis but also the prediction of complications and therapy outcomes of phaeochromocytomas in the future. Currently, the potential of radiomics and machine learning does not match expectations and awaits its fulfilment.

• MeSH
• Pheochromocytoma * diagnostic imaging   MeSH
• Humans MeSH
• Adrenal Gland Neoplasms * diagnostic imaging   MeSH
• Paraganglioma * diagnostic imaging   MeSH
• Tomography, X-Ray Computed methods   MeSH
• Image Processing, Computer-Assisted methods   MeSH
• Radiomics MeSH
• Machine Learning MeSH
• Artificial Intelligence MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Review MeSH

• Keywords
• naivní model, transfuzní přípravek,
• MeSH
• Blood Donors MeSH
• Blood Banking organization & administration   MeSH
• Machine Learning supply & distribution   MeSH
• Planning Techniques MeSH
• Artificial Intelligence supply & distribution   MeSH
• Equipment and Supplies, Hospital MeSH

• Conspectus
• Patologie. Klinická medicína
• NML Publication type
• závěrečné práce

Accelerated epigenetic aging has been associated with changes in cognition. However, due to the lack of neuroimaging epigenetics studies, it is still unclear whether accelerated epigenetic. Aging in young adulthood might underlie the relationship between altered brain dynamics and cognitive functioning. We conducted neuroimaging epigenetics follow-up of the European Longitudinal Study of Pregnancy and Childhood (ELSPAC) prenatal birth cohort in young adulthood and tested the possible mediatory role of accelerated epigenetic aging in the relationship between dynamic functional connectivity (DFC) and worse cognition. A total of 240 young adults (51% men; 28-30 years, all of European ancestry) participated in the neuroimaging epigenetics follow-up. Buccal swabs were collected to assess DNA methylation and calculate epigenetic aging using Horvath's epigenetic clock. Full-scale IQ was assessed using the Wechsler adult intelligence scale (WAIS). Resting-state functional magnetic resonance imaging (rs-fMRI) was acquired using a 3T Siemens Prisma MRI scanner, and DFC was assessed using mixture factor analysis, revealing information about the coverage of different DFC states. In women (but not men), lower coverage of DFC state 4 and thus lower frequency of epochs with high connectivity within the default mode network and between default mode, fronto-parietal, and visual networks was associated with lower full-scale IQ (AdjR2 = 0.05, std. beta = 0.245, p = 0.008). This relationship was mediated by accelerated epigenetic aging (ab = 7.660, SE = 4.829, 95% CI [0.473, 19.264]). In women, accelerated epigenetic aging in young adulthood mediates the relationship between altered brain dynamics and cognitive functioning. Prevention of cognitive decline should target women already in young adulthood.

• MeSH
• Default Mode Network * diagnostic imaging   physiology   MeSH
• Adult MeSH
• Epigenesis, Genetic * physiology   MeSH
• Intelligence * physiology   MeSH
• Cognition * physiology   MeSH
• Connectome * MeSH
• Humans MeSH
• Longitudinal Studies MeSH
• Magnetic Resonance Imaging MeSH
• DNA Methylation MeSH
• Young Adult MeSH
• Brain * diagnostic imaging   physiology   MeSH
• Nerve Net * diagnostic imaging   physiology   MeSH
• Aging * physiology   genetics   MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Young Adult MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

• MeSH
• Generative Artificial Intelligence * MeSH
• Communication MeSH
• General Practice MeSH
• Models, Theoretical MeSH
• Medical Informatics Applications MeSH
• Computer Security MeSH

• MeSH
• Hypertension MeSH
• Humans MeSH
• Smoking Cessation * MeSH
• Disability Evaluation MeSH
• Artificial Intelligence MeSH
• Check Tag
• Humans MeSH
• Publication type
• Examination Questions MeSH

Umělá inteligence (AI) se stále častěji uplatňuje v radiologii, kde nabízí potenciál zlepšit přesnost a efektivitu diagnostiky, zejména při hodnocení běžných zobrazovacích metod, jako jsou rtg snímky hrudníku. Tato studie analyzuje přesnost komerčního softwaru využívajícího strojové učení, respektive metody umělé inteligence, při detekci abnormalit na rtg snímcích hrudníku ve srovnání s nezávislými hodnoceními 3 juniorních radiologů. Výzkum byl proveden ve spolupráci s Nemocnicí Tábor, která poskytla dataset 207 anonymizovaných rtg snímků, z nichž 196 bylo vyhodnoceno jako relevantní. Senzitivita a specificita AI byla porovnána s lidským hodnocením v 5 kategoriích abnormalit: atelektáza (ATE), konsolidace (CON), zvětšení srdečního stínu (CMG), pleurální výpotek (EFF) a plicní léze (LES). Software Carebot AI CXR dosáhl vysoké senzitivity ve všech hodnocených kategoriích (např. ATE: 0,909; CMG: 0,889; EFF: 0,951), přičemž jeho přesnost byla konzistentní napříč všemi nálezy. Naopak specificita AI byla v některých kategoriích nižší (např. EFF: 0,792; CON 0,895), zatímco u radiologů dosahovala ve většině případů hodnot blížících se 1,000 (např. RAD 1 a RAD 2 EFF: 1,000). AI vykazovala konzistentně vyšší senzitivitu než méně zkušení radiologové (např. RAD 1 ATE: 0,087; CMG: 0,327) a v některých případech i než zkušenější hodnotitelé, avšak za cenu mírného snížení specificity. Studie zahrnuje také kazuistiky, včetně falešně pozitivních a falešně negativních nálezů, které přispívají k hlubšímu pochopení přesnosti AI v klinické praxi. Výsledky naznačují, že AI může efektivně doplňovat práci radiologů, zejména u méně zkušených lékařů, a zlepšit senzitivitu diagnostiky na rtg snímcích hrudníku.

Artificial intelligence (AI) has been increasingly applied in radiology, where it offers the potential to improve the accuracy and efficiency of diagnosis, particularly in the evaluation of conventional imaging modalities such as chest X-rays. This study analyzes the performance of commercial software using machine learning and, respectively, artificial intelligence approaches (Carebot AI CXR; Carebot s.r.o.) in detecting abnormalities in chest radiographs compared with independent evaluations by 3 radiologists of different levels of experience. The study was conducted in collaboration with Hospital Tabor, which provided a dataset of 207 anonymised radiographs, out of which 196 were assessed as relevant. The sensitivity and specificity of AI were compared with human assessment in 5 categories of abnormalities: atelectasis (ATE), consolidation (CON), cardiac shadow enlargement (CMG), pleural effusion (EFF) and pulmonary lesions (LES). Carebot AI CXR software achieved high sensitivity in all evaluated categories (e.g., ATE: 0.909, CMG: 0.889, EFF: 0.951), and its performance was consistent across all findings. In contrast, AI specificity was lower in some categories (e.g., EFF: 0.792, CON: 0.895), while radiologists achieved performance values approaching 1.000 in most cases (e.g., RAD 1 and RAD 2 EFF: 1.000). AI demonstrated consistently higher sensitivity than less experienced radiologists (e.g., RAD 1 ATE: 0.087, CMG: 0.327) and in some cases than more experienced assessors, but at a modest decrease in specificity. The study also includes case reports, including false-positive and false-negative findings, which contribute to a deeper understanding of AI performance in clinical practice. The results suggest that AI can effectively complement the work of radiologists, especially for less experienced doctors, and improve the sensitivity of diagnosis on chest radiographs.

Tento článek zpracovává téma nových trendů a technologií v urologii, a to konkrétně v oblasti telemedicíny a umělé inteligence. Nejprve stručně pojednává o přínosech telemedicíny a jak mění pohled na vztah mezi lékařem a pacientem. Podrobněji se pak text věnuje především umělé inteligenci, jež se v současnosti dostává do popředí zájmu laické i odborné veřejnosti. Její potenciál v urologii je testován v mnoha studiích, především se zaměřením na uroonkologii, v menší míře pak také v oblasti benigních urologických onemocnění. Článek se snaží identifikovat nejvýznamnější pokroky v této rychle se rozvíjející oblasti, a zároveň předkládá současné limity jejího zapojení do klinické praxe.

This article explores the emerging trends and technologies in urology, focusing on telemedicine and artificial intelligence. It provides a brief overview of the benefits of telemedicine and its impact on the patient-physician interactions. The article subsequently explores in detail the use of artificial intelligence, which is currently gaining considerable interest from both general public and medical professionals. Its potential in urology has been tested in a number of clinical studies, particularly in the field of uro-oncology and, to a lesser extent, in benign urological diseases. The aim of this article is to identify the key advances in this rapidly evolving field, while also highlighting the current limitations of its implementation into clinical practice.

• MeSH
• Deep Learning MeSH
• Humans MeSH
• Robotic Surgical Procedures MeSH
• Machine Learning MeSH
• Telemedicine MeSH
• Artificial Intelligence MeSH
• Urologic Neoplasms diagnosis   therapy   MeSH
• Urology * trends   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Review MeSH