The AI-AGE project was presented at the round table “Artificial Intelligence in Healthcare – Challenges and Opportunities”, held on 24 April 2026 at the Montenegrin Academy of Sciences and Arts (CANU) in Podgorica. The event gathered experts from Montenegro and Bosnia and Herzegovina to discuss the role of AI in healthcare, including clinical applications, digital transformation, ethics, medical imaging, NLP, and AI assistants.
AI-AGE presented at CAN Round Table
AI-AGE was presented by Prof. Dr Nataša Popović, Faculty of Medicine, University of Montenegro, in the session dedicated to AI in clinical practice. The presentation highlighted key findings of the project and demonstrated how AI can support early detection and screening of chronic diseases, including examples related to colorectal cancer detection and the use of biomarkers.
Opportunity to present goals and results of the project
The event was also an opportunity to promote EuroCC activities and the role of NCC Montenegro in strengthening national capacities in HPC, HPDA, and AI. Participation in this round table further positioned AI-AGE within the broader regional discussion on responsible and clinically relevant use of artificial intelligence in medicine.
Key findings and potential benefits of the use of AI models developed in AI-AGE
Our team presented a paper titled “Interpretable ML for Diabetes and Prediabetes Screening Using Self-Reported Health Indicators” by S. Lazic, S. Cakic, I. Rubezic Lukic, N. Popovic, and T. Popovic at the 30. Annual Conferenc on Information Technology IT 2026. This was part of mentoring activities and efforts related to development of young researchers.
The paper was presented at the conference by Ms. Sanja Lazic (MSc candidate)
ABSTRACT – Early identification of type 2 diabetes (T2D) and prediabetes enables timely interventions, yet screening often relies on self-reported data rather than laboratory testing. This work compares lightweight Machine Learning (ML) models: Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Multilayer Perceptron (MLP) trained on 21 self-reported indicators from the 2015 Behavioral Risk Factor Surveillance System (BRFSS) dataset for three-class classification (no diabetes, prediabetes, diabetes). We propose a screening-oriented evaluation where a probability threshold is selected to achieve a target sensitivity (recall) of 0.80. LightGBM achieves balanced accuracy of 0.52 and precision of 0.33 at the target sensitivity, with 38% of cases flagged. Tree SHapley Additive exPlanations (TreeSHAP) highlight general health status, age category, body mass index (BMI), and hypertension as dominant predictors. A FastAPI web application provides individual risk estimates and instance-level explanations. The pipeline demonstrates feasibility of interpretable, calibrated screening from non-laboratory data.
AI-AGE team represenatives participated in the various events and activities at IT2026 conference in Zabljak. We ook part in discussions related to Lessons Learned for HPC and AI applications in various domains in Montenegro. We also participated in the special session dedicated to project results presentations where we had a chance to discuss the project with researchers from Montenegro and the region.
AI-AGE was featured at the project results presentations
We particiapted in EuroCC4SEE panel on Lessons Learned for HPC and AI applications
High-Performance Computing (HPC) and Artificial Intelligence (AI) are rapidly transforming the landscape of healthcare — moving far beyond research prototypes into solutions that can shape clinical practice and improve patient outcomes. This transition from strategy to real-world impact was the focus of the recent EuroCC2 initiative “Symposium: HPC and AI Driven Innovation in Healthcare”. AI-AGE team participated in organization, coordination, and presentations at this event.
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The Vision: Bridging Strategy and Clinical Practice
The event was organized in cross-collaboration with EuroCC2 & EuroCC4SEE
AI-AGE project was presented and further collaboration with EuroCC3 was discussed
Healthcare is today generating vast volumes of data — from medical imaging to electronic health records, genomics, wearable sensors and beyond. HPC provides the computational power needed to process and analyse this data at scale, while AI techniques such as deep learning unlock patterns that are invisible to traditional analysis methods. Together, HPC and AI form a powerful synergy for healthcare innovation:
Accelerated diagnostics: AI models trained on large annotated datasets can assist clinicians by accurately identifying disease signs in imaging and other modalities.
Biomarker discovery and precision medicine: High-throughput computing enables the discovery of subtle biological signals indicative of disease progression or treatment response.
Predictive and personalised care: HPC-enabled AI workflows can predict patient outcomes and support real-time clinical decision making.
Symposium included presentations of successs stories from the region
This strategic capability — from data to insights to impact — was the core theme explored through EuroCC2 activities in Montenegro and the wider South-Eastern Europe region.
Key Takeaways for Healthcare Innovation
From Research to Clinical Utility HPC and AI solutions are no longer confined to laboratories. With appropriate infrastructure, data governance and clinical integration pathways, these technologies are being translated into tools that support healthcare professionals in diagnosis and treatment.
Regional and Cross-Institutional Collaboration The EuroCC2 framework — including the National Competence Centre Montenegro — brings together academic institutions, healthcare providers, and technology partners to share resources, expertise, and training. These collaborative ecosystems are essential for building sustainable HPC-AI capacity in healthcare.
Capacity Building and Skills Development One of the crucial pillars of impactful HPC and AI adoption is training. Workshops, seminars, and hands-on sessions equip researchers, clinicians, and students with the skills to leverage HPC and AI tools effectively in their domains.
Enabling Infrastructure Access Through EuroCC2 and related programmes, researchers and practitioners gain access to European HPC resources — reducing barriers to entry for high-end computing and enabling complex analyses that were previously impractical.
What This Means for Montenegro and Beyond
Montenegro, alongside partner regions across Europe, is building the foundation for a healthcare ecosystem that integrates HPC and AI into everyday clinical workflows. By investing in strategic computing infrastructure, enabling cross-sector collaboration, and fostering technical expertise, the potential to improve patient outcomes, streamline clinical processes, and support data-driven medicine is growing stronger.
The event gathered representatives from Healthcare and IT sectors already involved in research and development of HPC and AI driven solutions for healthcare and medical research. More info at NCC Montenegro site: [link].
Over 20 participants in the Symposium, important discussion of next steps
High-Performance Computing (HPC) and Artificial Intelligence (AI) are increasingly moving beyond research laboratories into real clinical environments. Across Montenegro and the SEE region, promising AI solutions have been developed for medical image analysis, biomarker detection, and predictive diagnostics. The critical challenge today is ensuring their structured transition from research prototypes to validated, deployable tools within healthcare systems.
Symposium on HPC and AI in Healthcare and Medicine co-organiozed by Ai-AGE
This event addresses precisely that transition. It focuses on how HPC infrastructure, interdisciplinary collaboration, and coordinated ecosystem support can accelerate the integration of AI into everyday clinical practice. Particular attention will be given to available computational capacities, real-life use cases, and pathways toward sustainable deployment.
The event is organized as a joint initiative between NCC Montenegro and NCC Bosnia and Herzegovina, within the broader framework of EuroCC 2 and EuroCC4SEE. It also represents a form of cross-project pollination with the AI-AGE project, demonstrating how research-driven innovation can evolve into applied healthcare solutions through regional cooperation.
AI-AGE will be featured in the presentation session
Researchers, clinicians, innovators, and industry partners are invited to join the discussion, exchange expertise, and contribute to shaping the next steps for HPC- and AI-driven healthcare across Southeast Europe. The event is scheduled for Friday, 13 Feb 2026. Please contact NCC Montenegro for further details.
The AI-AGE project will be actively featured at the INFORMATION TECHNOLOGIES 2026 (IT 2026), the 30th edition of a long-standing international scientific and professional conference bringing together academia, industry, and the wider innovation ecosystem. The conference taktes place in Zabljak, 24-28 February.
As part of the project presentation session, AI-AGE will showcase its ongoing research on AI-driven biomarkers of ageing, with a particular focus on advanced data analytics, computer vision, and high-performance computing applied to biomedical and health-related domains. This session provides an excellent opportunity to present project objectives, methodologies, and early scientific insights to a broad and interdisciplinary audience.
Click on image to open IT2026 website
In addition to the project presentation, AI-AGE researchers will actively participate in the conference program, contributing to scientific discussions and knowledge exchange across multiple sessions. Importantly, young researchers involved in the AI-AGE project will present a scientific paper, highlighting the project’s strong commitment to capacity building, early-stage researcher development, and excellence in applied AI research.
In line with the AI-AGE project’s commitment to open science and transparency, the dataset associated with our newly published BMJ Open paper has been made publicly available on Zenodo.
The dataset includes anonymised primary care data on frailty indicators, chronic diseases, and demographic variables among adults aged 40–69 in Montenegro. It provides a valuable resource for researchers working on biological ageing, frailty, multimorbidity, and AI-driven risk modelling, particularly in middle-income and transitional health systems.
By sharing this dataset, AI-AGE aims to support international collaboration, reproducibility, and secondary analyses that can further advance non-invasive ageing research.
At the September team meeting, members discussed recent progress and upcoming project goals. The team submitted a journal paper on AI software for image quality assessment, and we are working on refining predictive modeling for Alzheimer’s disease and colorectal cancer. The initial results are promissing, but more experimenting is on the way.
The team also agreed to extend the Biobank subscription for two more years, ensuring continued access to essential datasets. With several manuscripts nearing submission, AI-AGE continues to strengthen its interdisciplinary collaboration between technology and healthcare.
The Ministry of Education, Science and Innovation of Montenegro has officially reviewed and approved the Annual Progress Report for the first research year of the project AI-AGE – Artificial Intelligence Supported Identification of Novel Non-invasive Biomarkers of Aging. The report highlights successful completion of all planned activities, including establishment of research infrastructure, expanded access to the UK Biobank, advanced AI/ML experimentation, and the initiation of interdisciplinary doctoral research.
Following the first successful year, AI-AGE will continue for two more years in accordance with the approved work plan. The focus for the next phase will be on validating early research results, publishing scientific papers, and strengthening international cooperation. The project continues to play a key role in advancing Montenegro’s research capacities in artificial intelligence and biomedicine through collaboration between the University of Donja Gorica and the Faculty of Medicine, University of Montenegro.
Official confirmation of the project implementation for the first year
At the Department of Medical Physiology, Faculty of Medicine, University of Montenegro, groundbreaking research is being conducted to address a growing health challenge: frailty and its connection to multimorbidity in middle-aged individuals. While traditionally associated with older adults, frailty is increasingly recognized as a critical issue for younger age groups, with significant implications for healthcare systems.
Exploring Frailty and Multimorbidity: Advancing Healthcare for Middle-Aged Populations in Montenegro
🔍 Why This Research Matters:
Frailty and multimorbidity are major contributors to increased mortality rates and healthcare utilization, creating a substantial burden on individuals and health systems alike. By examining these issues in a middle-aged Montenegrin population, this research aims to fill a critical knowledge gap and pave the way for more proactive healthcare approaches.
🎯 Main Goals of the Study:
Understand Frailty Prevalence: Investigate how frailty impacts middle-aged individuals and its relationship with chronic and infectious diseases.
Enhance Healthcare Strategies: Explore patterns in healthcare utilization to better address the dual challenges of frailty and multimorbidity.
Strengthen Health Systems: Provide actionable insights to support early identification and timely interventions for at-risk individuals, particularly in low- and middle-income settings.
This research emphasizes the urgent need to integrate frailty and multimorbidity assessments into primary healthcare, enabling tailored interventions that improve health outcomes for vulnerable populations. By focusing on preventive care and system strengthening, we can work toward reducing the burden of chronic diseases and enhancing the resilience of healthcare systems.
🌍 Building a Healthier Future:
The AI-AGE team is committed to advancing medical research that addresses pressing public health challenges. Together, we aim to create a more sustainable and equitable healthcare system for all.
