BACKGROUND: Endoscopic retrograde cholangiopancreatography (ERCP) still has a relatively high complication rate, underscoring the importance of high-quality training. Despite existing guidelines, real-world data on training conditions remain limited. This pan-European survey aims to systematically explore the perceptions surrounding ERCP training. METHODS: A survey was distributed through the friends of United European Gastroenterology (UEG) Young Talent Group network to physicians working in a UEG member or associated states who regularly performed ERCPs. RESULTS: Of 1035 respondents from 35 countries, 649 were eligible for analysis: 228 trainees, 225 trainers, and 196 individuals who regularly performed ERCP but were neither trainees nor trainers. The mean age was 43 years, with 72.1% identifying as male, 27.6% as female, and 0.3% as non-binary. The majority (80.1%) agreed that a structured training regimen is desirable. However, only 13.7% of trainees and 28.4% of trainers reported having such a structured program in their institutions. Most respondents (79.7%) supported the concept of concentrating training in centers meeting specific quality metrics, with 64.1% suggesting a threshold of 200 annual ERCPs as a prerequisite. This threshold revealed that 36.4% of trainees pursued training in lower-volume centers performing <200 ERCPs annually. As many as 70.1% of trainees performed <50 annual ERCPs, whereas only 5.0% of trainers performed <50 ERCPs annually. A low individual trainee caseload (<50 ERCPs annually) was more common in lower-volume centers than in higher-volume centers (82.9% vs. 63.4%). CONCLUSIONS: The first pan-European survey investigating ERCP training conditions reveals strong support for structured training and the concentration of training efforts within centers meeting specific quality metrics. Furthermore, this survey exposes the low availability of structured training programs with many trainees practicing at lower-volume centers and 71% of all trainees having little hands-on exposure. These data should motivate to standardize ERCP training conditions further and ultimately improve patient care throughout Europe.

• MeSH
• Cholangiopancreatography, Endoscopic Retrograde * standards   adverse effects   MeSH
• Adult MeSH
• Gastroenterology * education   MeSH
• Clinical Competence standards   MeSH
• Middle Aged MeSH
• Humans MeSH
• Surveys and Questionnaires statistics & numerical data   MeSH
• Education, Medical, Graduate * standards   methods   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Geographicals
• Europe MeSH

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

Zobrazovací metoda pozitronové tomografie s výpočetní tomografií (positron emission tomography / computed tomography, PET/CT) s využitím prostatického specifického membránového antigenu (prostate-specific membrane antigen, PSMA), PSMA PET/CT, představuje zásadní pokrok v diagnostice a léčbě karcinomu prostaty. V oblasti diagnostiky nabízí výrazně vyšší senzitivitu a specificitu oproti konvenčním metodám, zejména při primárním stagingu, perzistenci prostatického specifického antigenu (PSA) po radikální prostatektomii a biochemickém relapsu po radikální léčbě. Stále častěji se uplatňuje i v rámci teranostiky, tedy propojení diagnostiky a cílené radionuklidové terapie u pacientů s metastazujícím kastračně rezistentním karcinomem prostaty. Klinické studie fáze III (např. VISION, TheraP, PSMAfore) prokázaly významný přínos PSMA značeného luteciem-177 (177Lu-PSMA-617) z hlediska přežití i kvality života ve srovnání s jinými léčebnými možnostmi. Využití PSMA PET/CT tak výrazně přispívá k personalizovanému přístupu k pacientům a mění zavedené algoritmy diagnostiky a léčby karcinomu prostaty.

The imaging modality prostate specific membrane antigen positron emission tomography / computed tomography (PSMA PET/CT) represents a major advancement in the diagnosis and treatment of prostate cancer. In diagnostics, it offers significantly higher sensitivity and specificity compared to conventional methods, particularly in primary staging, persistent prostate specific antigen (PSA) following radical prostatectomy, and biochemical recurrence after radical treatment. It is increasingly utilized in theranostics, which combines diagnostic imaging with targeted radionuclide therapy in patients with metastatic castration-resistant prostate cancer. Phase III clinical trials (e.g. VISION, TheraP, PSMAfore) have demonstrated a significant benefit of 177Lu-PSMA-617 in terms of survival and quality of life compared to other treatment options. The use of PSMA PET/CT thus contributes substantially to personalized patient care and is reshaping established diagnostic and therapeutic algorithms in prostate cancer management.

• 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

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

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

Článek prezentuje dílčí úspěchy v rámci intervence zrakového terapeuta / tyflopeda v jedné osobě a klinického logopeda v součinnosti s ergoterapeutem u klientky s kombinovaným postižením v oblasti sebesycení a nastavení režimových opatření k řešení projevů dysfagie. Kromě aspektu samostatného přijímání stravy jsou zdůrazněny i další specifické oblasti, v nichž je podporována samostatnost klientky s cílem dosáhnout co nejvyšší míry aktivizace. Cílem příspěvku je popsat proces nácviku sebesycení společně s jeho problematickými oblastmi včetně průběhu nastavování režimových opatření souvisejících také s rozvojem prostorové orientace, a to tak, aby tato opatření vyhovovala konkrétní klientce v kontextu poskytování sociální služby. Význam popsané intervence je klíčový z hlediska podpory soběstačnosti osoby se zdravotním postižením ve smyslu cílů poskytování sociálních služeb stejně jako monitorování bezpečnosti procesu příjmu potravy a tekutin.

The article presents partial successes within the framework of the intervention of a vision therapist/therapist for the visually impaired in one person and a clinical speech therapist in collaboration with an occupational therapist, for a client with a combined disability in the area of self-feeding and setting regimen measures to deal with dysphagia symptoms. In addition to the aspect of independent food intake, other specific areas are also emphasised in which the client's independence is supported, with the aim of achieving the highest possible level of activation. The aim of the article is to describe the process of training self-feeding, together with its problematic areas, including the process of setting regimen measures related to the development of spatial orientation so that they suit a particular client in the context of social services provided. The importance of the intervention described above is crucial in terms of supporting the self-sufficiency of a person with a disability, with reference to the goals of providing social services, as well as of monitoring the safety of the process of food and fluid intake.

• MeSH
• Adult MeSH
• Occupational Therapy MeSH
• Humans MeSH
• Intellectual Disability complications   MeSH
• Deglutition Disorders diagnosis   rehabilitation   therapy   MeSH
• Feeding and Eating Disorders diagnosis   therapy   MeSH
• Persons with Disabilities MeSH
• Eating MeSH
• Psychiatric Rehabilitation MeSH
• Psychological Tests MeSH
• Independent Living * MeSH
• Social Work MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Female MeSH
• Publication type
• Case Reports MeSH

IMPORTANCE: The successful implementation of artificial intelligence (AI) in health care depends on its acceptance by key stakeholders, particularly patients, who are the primary beneficiaries of AI-driven outcomes. OBJECTIVES: To survey hospital patients to investigate their trust, concerns, and preferences toward the use of AI in health care and diagnostics and to assess the sociodemographic factors associated with patient attitudes. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study developed and implemented an anonymous quantitative survey between February 1 and November 1, 2023, using a nonprobability sample at 74 hospitals in 43 countries. Participants included hospital patients 18 years of age or older who agreed with voluntary participation in the survey presented in 1 of 26 languages. EXPOSURE: Information sheets and paper surveys handed out by hospital staff and posted in conspicuous hospital locations. MAIN OUTCOMES AND MEASURES: The primary outcome was participant responses to a 26-item instrument containing a general data section (8 items) and 3 dimensions (trust in AI, AI and diagnosis, preferences and concerns toward AI) with 6 items each. Subgroup analyses used cumulative link mixed and binary mixed-effects models. RESULTS: In total, 13 806 patients participated, including 8951 (64.8%) in the Global North and 4855 (35.2%) in the Global South. Their median (IQR) age was 48 (34-62) years, and 6973 (50.5%) were male. The survey results indicated a predominantly favorable general view of AI in health care, with 57.6% of respondents (7775 of 13 502) expressing a positive attitude. However, attitudes exhibited notable variation based on demographic characteristics, health status, and technological literacy. Female respondents (3511 of 6318 [55.6%]) exhibited fewer positive attitudes toward AI use in medicine than male respondents (4057 of 6864 [59.1%]), and participants with poorer health status exhibited fewer positive attitudes toward AI use in medicine (eg, 58 of 199 [29.2%] with rather negative views) than patients with very good health (eg, 134 of 2538 [5.3%] with rather negative views). Conversely, higher levels of AI knowledge and frequent use of technology devices were associated with more positive attitudes. Notably, fewer than half of the participants expressed positive attitudes regarding all items pertaining to trust in AI. The lowest level of trust was observed for the accuracy of AI in providing information regarding treatment responses (5637 of 13 480 respondents [41.8%] trusted AI). Patients preferred explainable AI (8816 of 12 563 [70.2%]) and physician-led decision-making (9222 of 12 652 [72.9%]), even if it meant slightly compromised accuracy. CONCLUSIONS AND RELEVANCE: In this cross-sectional study of patient attitudes toward AI use in health care across 6 continents, findings indicated that tailored AI implementation strategies should take patient demographics, health status, and preferences for explainable AI and physician oversight into account.

• MeSH
• Adult MeSH
• Trust MeSH
• Internationality MeSH
• Middle Aged MeSH
• Humans MeSH
• Hospitals MeSH
• Delivery of Health Care * MeSH
• Cross-Sectional Studies MeSH
• Surveys and Questionnaires MeSH
• Aged MeSH
• Artificial Intelligence * MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

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
• Fuchs' Endothelial Dystrophy * diagnosis   therapy   MeSH
• Humans MeSH
• Tomography, Optical Coherence methods   MeSH
• Artificial Intelligence * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Systematic Review MeSH