Lithuanian University of Health Sciences (Lithuanian: Lietuvos sveikatos mokslų universitetas, LSMU) is a medical school in Kaunas, Lithuania. The present-day Lithuanian University of Health Sciences is a consolidation of two institutions of higher education, Kaunas University of Medicine and the Lithuanian Veterinary Academy. It uses the Hospital of Lithuanian University of Health Sciences Kaunas Clinics and the Kaunas Red Cross Hospital as teaching hospitals..
To explore how non-geriatric specialists across UEMS bodies who care for older adults understand and approach frailty in order to guide future interspecialty educational initiatives. An online survey was conducted to capture their perspectives, practices and training needs regarding frailty. Non-geriatric specialists recognise the importance of frailty but report limited confidence and training, indicating a clear need for accessible and standardised education. Frailty in older adults is associated with increased vulnerability, poorer outcomes, and greater healthcare utilisation. To inform future inter-specialty educational initiatives, a survey across UEMS bodies was conducted. An online survey was disseminated between July and November 2025 via the UEMS Coordination team to all sections, divisions, multidisciplinary joint committees, and thematic federations. Eligible respondents were specialists not certified in Geriatric Medicine who provide care to older adults (≥ 65 years). Of 416 respondents, 283 were non-geriatric specialists caring for older people. They encompassed 40 specialties (40
Background: Artificial intelligence is emerging as a promising tool in surgical oncology, with growing evidence suggesting potential applications in diagnostic support, intraoperative guidance, and perioperative risk assessment. In gastric cancer surgery, emerging applications range from AI-assisted endoscopic detection to data-driven perioperative risk prediction, while some technological developments, particularly in robotic autonomy, derive from broader surgical or experimental models that may inform future gastric procedures. Methods: A narrative review was conducted following established methodological standards, including the Scale for the Assessment of Narrative Review Articles (SANRA) and the Search-Appraisal-Synthesis-Analysis (SALSA) framework. English-language studies indexed in PubMed, Scopus, Embase, and Web of Science up to October 2025 were included. Evidence was synthesized thematically across five domains: AI-assisted anatomical recognition and lymphadenectomy support, autonomous robotic systems, early cancer detection, perioperative predictive and frailty models, and ethical and regulatory considerations. Results: AI-based computer vision and deep learning algorithms have demonstrated promising capabilities for real-time anatomical recognition, surgical phase classification, and intraoperative guidance, although evidence of direct patient-level benefit remains limited. In diagnostic settings, AI-assisted endoscopy and Raman spectroscopy have been shown to improve early lesion detection and reduce dependence on operator experience. Predictive models, including MySurgeryRisk and AI-driven frailty assessments, may support individualized prehabilitation planning and perioperative risk stratification. Persistent limitations include small and heterogeneous datasets, insufficient external validation, and unresolved concerns related to data privacy, algorithmic interpretability, and medico-legal responsibility. Conclusions: Artificial intelligence is progressively emerging as a promising tool in gastric cancer surgery, integrating automation, advanced analytics, and human clinical reasoning. Its safe and ethical adoption requires robust validation, transparent governance, and continuous surgeon oversight. When developed within human-centered and ethically grounded frameworks, AI can augment, rather than replace, surgical expertise, potentially advancing precision, safety, and equity in oncologic care.
Introduction Geriatric Medicine (GM) has evolved in Europe over the past few decades, although variably across countries. The aim of this paper is to explore the GM specialty status, postgraduate and undergraduate education for medical students, nurses and Allied Health Professionals (AHPs), academic development and GM clinical services in Europe.Methods We collected cross-sectional data from 38 European countries as listed by the World Health Organization with an online quantitative survey. This study is part of the PROGRAMMING COST Action, which stands for PROmoting GeRiAtric Medicine in countries where it is still eMergING and is funded by the European Cooperation for Science and Technology.Results GM is recognised as a distinct specialty in 24 countries (63.15%), while specialty training is available in 21 countries (55.26%). Principles of GM are included in the undergraduate curricula for medical students in all or most medical universities in 24 countries (63.15%), for nurses in 26 countries (68.42%) and for AHPs in 19 countries (50%). Geriatric hospital wards are present in 28 countries (73.68%), and in eight countries, there are no GM clinical services available, especially in Southern Europe. GM is a standalone academic discipline in 20 countries (52.63%), and advanced academic doctoral study is available in 18 countries (47.36%). GM is more established in Northern and Western Europe.Conclusion Our study highlights the inconsistent development of GM across European countries. It provides a foundation for policy development and educational reform to meet the needs of Europe's ageing population.
BACKGROUND:The worldwide acceleration of digital transformation in healthcare underscores the need for professionals to continuously adapt and sustain robust digital health competence, shaped not only by individual characteristics and institutional environments but also by broader social, cultural, and geopolitical factors. OBJECTIVE:This study aimed to identify distinct clusters of digital health competence among healthcare professionals across 19 diverse countries and regions, and to examine the factors influencing the development and distribution of these competence clusters. METHODS:A cross-sectional international survey study was conducted between 2023 and 2024, using a validated survey instrument measuring digital health competence and its influencing factors. Data were collected from healthcare professionals in 19 countries and regions (n = 6440; n = 5945 used for this study), following a harmonised protocol with shared demographic templates and instruments. K-means cluster analysis was employed to derive digital competence profiles, with comparative analyses conducted to investigate associations between the identified clusters and individual characteristics (e.g., age, education, professional experience). RESULTS:Five distinct clusters of digital health competence were identified: (1) Beginners, (2) Developing Professionals, (3) Emerging Users, (4) Proficient Practitioners, and (5) Pioneers. Higher competence clusters (4 and 5) were associated with younger age, higher education, hospital-based work, and stronger perceived support from management, organisational structures, and colleagues. In contrast, lower-performing clusters reported limited digital engagement and minimal support. Perceived leadership influence, particularly managerial commitment to digital change, was a key differentiator across clusters. CONCLUSIONS:The findings demonstrate substantial variation in digital health competence across healthcare professionals internationally. Cluster-specific strategies, such as targeted upskilling, peer mentoring, and leadership engagement, are needed to address competence gaps. The results provide a foundation for policy development and workforce training frameworks aimed at strengthening digital readiness in global healthcare systems. Future research should explore longitudinal competence development and evaluate targeted interventions.
To present and contextualise the 2025 revision of the European Training Requirements (ETR) for the Specialty of Geriatric Medicine, developed under the auspices of the European Union of Medical Specialists-Geriatric Medicine Section (UEMS-GMS), and to summarise its main innovations in structure, content, and pedagogical approach. The 2025 ETR strengthens the 2020 version by further developing Entrustable Professional Activities (EPAs) to harmonise postgraduate geriatric training across Europe. It updates and expands the content and recommends the knowledge-based European Geriatric Medicine Specialty Exam (EGeMSE) as part of the certification standards. The 2025 ETR reflects the continued evolution of European geriatric medicine education, uniting scientific progress and competency-based pedagogy within a coherent, evidence-informed framework that promotes excellence, mobility, and comparability of specialist training across Europe. It establishes a forward-looking standard designed to remain fit for purpose over the next 5 years. To describe the process, content, and significance of the 2025 revision of the European Training Requirements (ETR) for the Specialty of Geriatric Medicine, developed under the auspices of the European Union of Medical Specialists-Geriatric Medicine Section (UEMS-GMS). The revision aims to update European postgraduate training standards to reflect current scientific, clinical, and educational advances. The revision followed the official UEMS procedure for ETR development and was conducted by the UEMS-GMS ETR Review Committee between September 2024 and October 2025. Building on the 2019 European Postgraduate Curriculum in Geriatric Medicine and the 2020 ETR, the committee incorporated stakeholder feedback, expert consultation, and international endorsement. The final document was reviewed and formally approved by the UEMS-GMS and endorsed by the European Geriatric Medicine Society (EuGMS), the European Academy for Medicine of Ageing (EAMA), the International Association of Gerontology and Geriatrics (IAGG), and the European Interdisciplinary Council on Ageing (EICA). The 2025 ETR retains the established UEMS three-part structure (for trainees, trainers, and training institutions) whilst introducing a modernised, competency-based education framework. The updated syllabus expands and refines theoretical content to reflect the latest scientific, clinical, and pedagogical advances. The revised ETR further elaborates the use of Entrustable Professional Activities (EPAs) as core instruments for assessing competence. Assessment standards now feature an expanded toolkit, incorporating the knowledge-based European Geriatric Medicine Specialty Exam (EGeMSE). The 2025 ETR for Geriatric Medicine represents a further step toward harmonised, competency-based specialist training across Europe and beyond. Reflecting global public health priorities, including the UN Decade of Healthy Ageing (2021–2030), the revision reinforces educational excellence and the delivery of high-quality, integrated, person-centred care for older adults. It also supports the continued development of the specialty of Geriatric Medicine in countries where it is not yet established or remains emerging. LINK to ETR 2025: https://www.uems.eu/european-training-requirements (Geriatric Medicine, 2025/29).
